Gene expression signature for classification of kidney tumors

ABSTRACT

The present invention provides a method for classification of kidney tumors through the analysis of the expression patterns of specific microRNAs and nucleic acid molecules relating thereto. Classification according to a microRNA expression framework allows optimization of treatment, and determination of specific therapy.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. §119(e) to U.S.Provisional Application No. 61/086,483, filed Aug. 6, 2008 and U.S.Provisional Application No. 61/158,368, filed Mar. 8, 2009 which areherein incorporated by reference in their entirety.

FIELD OF THE INVENTION

The present invention relates to methods for classification of kidneytumors. Specifically the invention relates to microRNA moleculesassociated with specific kidney tumors, as well as various nucleic acidmolecules relating thereto or derived therefrom.

BACKGROUND OF THE INVENTION

In recent years, microRNAs (miRs) have emerged as an important novelclass of regulatory RNA, which have a profound impact on a wide array ofbiological processes. These small (typically 18-24 nucleotides long)non-coding RNA molecules can modulate protein expression patterns bypromoting RNA degradation, inhibiting mRNA translation, and alsoaffecting gene transcription. miRs play pivotal roles in diverseprocesses such as development and differentiation, control of cellproliferation, stress response and metabolism. The expression of manymiRs was found to be altered in numerous types of human cancer, and insome cases strong evidence has been put forward in support of theconjecture that such alterations may play a causative role in tumorprogression. The remarkable tissue-specificity of miR expression allowsthe development of novel approaches to molecular classification. Thereare currently about 885 known human miRs.

Renal cancers account for more then 3% of adult malignancies and causemore than 13,000 deaths per year in the US alone (Jemal, A., et al.2008, Cancer statistics, CA Cancer J Clin 58, 71-96). The incidence ofrenal cancers in the US rose more then 50% between 1983 to 2002 and theestimated number of new cases per year rose from 39,000 estimated in2006 to 54,000 estimated in 2008. Despite the trend of increasedincidence of relatively small and kidney-confined disease, the rate ofmortality has not changed significantly during the last 2 decades in theU.S. and Europe. In the 1980s, renal tumors were basically regarded asone disease: the higher the stage and the grade, the worse is theprognosis. After the 1980s, molecular biologists and pathologistsdescribed new entities with different morphological and biologicalcharacteristics. Evidence for different long-term prognosis for thesesubtypes makes the correct pathological diagnosis of a renal cancercritically important for the clinician. Currently, it is well acceptedthat renal cell carcinoma (RCC) is a family of carcinomas which arisefrom the epithelium of the renal tubules. The current classification ofrenal cell carcinoma includes four main types: conventional cell (alsoknown as clear cell), papillary, chromophobe, and collecting ductcarcinoma, as well as unclassified renal cell carcinoma. Oncocytoma is abenign subtype of RCC.

Conventional cell renal carcinoma is the most frequent subtype of RCCand accounts for 60-70% of cases and account for the majority of renalcell cancer specific mortality. The term “conventional cell” is used toreplace the name “clear cell”, because some types have eosinophiliccytoplasm, generating a more difficult diagnostic challenge. In tumorsof this type, a characteristic vascular network is commonly observed.The conventional cell carcinoma type is associated with germ line andsomatic mutations of the von Hippel-Lindau (VHL) suppressor gene, andsuch mutations may indicate a more favorable prognosis. Papillary RCCtypically consists of a central fibrovascular core with epithelialcovered papillae. It is subclassified into type 1 and 2 tumors thatdiffer in terms of morphology, genotype and clinical outcome.Genetically, this type of tumor is associated with polysomies ofchromosomes 7 or 17 and deficiency of Y. Chromophobe renal cellcarcinoma was included before 1986 in the group of conventional cellRCC. The typical form exhibits balloon cells with an abundant granularpale cytoplasm, or eosinophilic cytoplasm that resemble the cells ofoncocytoma. Such features as described above are characteristic of thehistological subtypes, but inter-observer variations limit the accuracyof histological classification, with some types identified with asensitivity of 70% or lower. Furthermore, underlying biologicalmechanisms playing important roles in these tumors are yet to beelucidated.

These different histological subtypes of RCC vary in their clinicalcourses and their prognosis, and different clinical strategies have beendeveloped for their management. Patients with conventional cell renalcarcinoma have a poorer prognosis, and differences may also existbetween the prognosis of patients with papillary or chromophobe RCC. Thehistological types arise through different constellations of geneticalterations, and show expression or mutation in different oncogenicpathways; they therefore offer different molecular candidates fortargeted therapy (e.g., mTOR, VEGF, KIT). Initial studies showdifferences in the responses of RCC subtypes to targeted therapies(Lopez-Beltran, A., et al. 2008, Semin Diagn Pathol 25, 232-44), andfuture therapies are likely to be individualized for the differenttypes. The correct identification of these subtypes is thereforeimportant choice of treatment, and for the selection of patients forclinical trials.

Based on the growing clinical demand for accurate diagnosis of RCCsubtypes, recent studies focused on the immunohistochemical profiling ofdifferent carcinomas. Allory et al lately described a subset of 12antibodies as base for classification of renal cell carcinomas. In thisreport AMACR, CK7 and CD10 had the most powerful classification treeswith 78%-87% of carcinomas correctly classified (Allory, Y., et al.2008, Histopathology 52, 158-66) Immunohistochemistry provides limitedinformation for distinguishing chromophobe RCC from oncocytoma. However,the increasing number of smaller tumors and needle-biopsy proceduresplaces a strain on immunohistochemical methods. In a recent large studyof 235 cases, more than 20% of the core needle biopsies werenondiagnostic (Shannon, B. A., et al. 2008, J Urol 180, 1257-61). Thisemphasized the need for developing additional types of molecular markersfor the classification of renal tumors and for their study.

SUMMARY OF THE INVENTION

The present invention provides nucleic acid sequences for use in theidentification, classification and diagnosis of particular subtypes ofkidney tumors. The nucleic acid sequences can also be used as prognosticmarkers for prognostic evaluation and determination of appropriatetreatment of a subject based on the abundance of the nucleic acidsequences in a biological sample.

The present invention further provides a method of classifying kidneytumors, the method comprising: obtaining a biological sample from asubject; determining expression of individual nucleic acids in apredetermined set of microRNAs; and classifying the specific subtype ofkidney tumor in said sample.

The present invention is based in part on using microRNA microarrayswith a training set of 71 formalin-fixed, paraffin-embedded (FFPE) renaltumor samples, and identifying microRNAs that have specific expressionlevels in distinct tumor types. Clustering showed a strong similaritybetween oncocytoma and chromophobe subtypes, and between papillary andconventional-cell tumors. By basing a classification algorithm on thisstructure, inherent biological correlations were followed, and couldachieve accurate classification using few microRNAs markers. A two-stepdecision-tree classifier was defined that uses expression levels of sixmicroRNAs: the first step uses expression levels of hsa-miR-210 (SEQ IDNO: 20) and hsa-miR-221 (SEQ ID NO: 25) to distinguish between the twopairs of subtypes; the second step uses either hsa-miR-200c (SEQ ID NO:34) with hsa-miR-139-5p (SEQ ID NO: 14) to identify oncocytoma fromchromophobe, or hsa-miR-31 (SEQ ID NO: 4) with hsa-miR-126 (SEQ ID NO:41) to identify conventional-cell (clear cell) from papillary tumors.Tested on an independent set of 56 samples, this classifier identifiedcorrectly 93% of the cases. These results were further validated byquantitative real-time PCR (qRT-PCR).

According to one aspect, the present invention provides a method for thedetection of a specific subtype of kidney tumor, the method comprising:obtaining a biological sample from a subject; determining an expressionprofile in said sample of nucleic acid sequences selected from the groupconsisting of SEQ ID NOS: 1-75, and a sequence having at least about 80%identity thereto; and comparing said expression profile to a referencevalue; whereby an altered expression levels of the nucleic acid sequenceallows the detection of the specific subtype of kidney tumor in saidsample.

According to certain embodiments, the nucleic acid sequences areselected from the group consisting of SEQ ID NOS: 4, 14, 20, 25, 34, 41,and a sequence having at least about 80% identity thereto.

According to certain embodiments, said specific subtype is selected fromthe group consisting of oncocytoma, clear cell (conventional) RCC,papillary RCC and chromophobe RCC.

According to some embodiments, said altered expression level is a changein a score based on a combination of expression levels of said nucleicacid sequences.

The invention further provides a method for distinguishing betweenbenign and malignant kidney tumor, the method comprising: obtaining abiological sample from a subject; determining in said sample anexpression profile of nucleic acid sequences selected from the groupconsisting of SEQ ID NOS: 1-27, 47, 49, a fragment thereof or a sequencehaving at least 80% identity thereto; and comparing said expressionprofile to a reference value; whereby a relative abundance of saidnucleic acid sequences allows the detection of said kidney tumor.

According to some embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 12-15, 18,19, 25-27 is indicative of the presence of benign kidney tumor.

According to other embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 1-11, 16,17, 20-24, 47, 49 is indicative of the presence of malignant kidneytumor.

The invention further provides a method for distinguishing betweenchromophobe RCC and oncocytoma, the method comprising: obtaining abiological sample from a subject; determining in said sample anexpression profile of nucleic acid sequences selected from the groupconsisting of SEQ ID NOS: 14-15, 28-35, a fragment thereof or a sequencehaving at least 80% identity thereto; and comparing said expressionprofile to a reference value; whereby a relative abundance of saidnucleic acid sequences allows the detection of said oncocytoma orchromophobe RCC.

According to some embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 14-15, 28-31is indicative of the presence of oncocytoma.

According to other embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 32-35 isindicative of the presence of chromophobe RCC.

The invention further provides a method for distinguishing between clearcell RCC and oncocytoma, the method comprising: obtaining a biologicalsample from a subject; determining in said sample an expression profileof nucleic acid sequences selected from the group consisting of SEQ IDNOS: 1-3, 6, 7, 11, 13, 16, 17, 19-27, 30, 31, 36-40, 47-49, a fragmentthereof or a sequence having at least 80% identity thereto; andcomparing said expression profile to a reference value; whereby arelative abundance of said nucleic acid sequences allows the detectionof said oncocytoma or clear cell RCC.

According to some embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NO: 13, 19,25-27, 30, 31, 36-40, 48 is indicative of the presence of oncocytoma.

According to other embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 1-3, 6, 7,11, 16, 17, 20-24, 47, 49 is indicative of the presence of clear cellRCC.

The invention further provides a method for distinguishing betweenchromophobe RCC and clear cell RCC, the method comprising: obtaining abiological sample from a subject; determining in said sample anexpression profile of nucleic acid sequences selected from the groupconsisting of SEQ ID NOS: 1-3, 6, 7, 22, 23, 32-37, 43, 44, a fragmentthereof or a sequence having at least 80% identity thereto; andcomparing said expression profile to a reference value; whereby arelative abundance of said nucleic acid sequences allows the detectionof said RCC.

According to some embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 1-3, 6, 7,22, 23 is indicative of the presence of clear cell RCC.

According to other embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 32-37, 43,44 is indicative of the presence of chromophobe RCC.

The invention further provides a method for distinguishing between clearcell RCC and papillary RCC, the method comprising: obtaining abiological sample from a subject; determining in said sample anexpression profile of nucleic acid sequences selected from the groupconsisting of SEQ ID NOS: 4, 5, 8, 9, 30, 31, 41, 42, 50, 51, a fragmentthereof or a sequence having at least 80% identity thereto; andcomparing said expression profile to a reference value; whereby arelative abundance of said nucleic acid sequences allows the detectionof said RCC.

According to some embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 41, 42 isindicative of the presence of clear cell RCC.

According to other embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 4, 5, 8, 9,30, 31, 50, 51, is indicative of the presence of papillary RCC.

The invention further provides a method for distinguishing betweenchromophobe RCC and papillary RCC, the method comprising: obtaining abiological sample from a subject; determining in said sample anexpression profile of nucleic acid sequences selected from the groupconsisting of SEQ ID NOS: 1-5, 8-11, 13, 24-27, 30-35, 43, 44, 47, 49,52, 53, a fragment thereof or a sequence having at least 80% identitythereto; and comparing said expression profile to a reference value;whereby a relative abundance of said nucleic acid sequences allows thedetection of said RCC.

According to some embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 13, 25-27,32-35, 43, 44, 52, 53, is indicative of the presence of chromophobe RCC.

According to some embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 1-5, 8-11,24, 30, 31, 47, 49 is indicative of the presence of papillary RCC.

The invention further provides a method for distinguishing betweenpapillary RCC and oncocytoma, the method comprising: obtaining abiological sample from a subject; determining in said sample anexpression profile of nucleic acid sequences selected from the groupconsisting of SEQ ID NOS: 4, 5, 8-13, 25-27, 41-42, 45-47, a fragmentthereof or a sequence having at least 80% identity thereto; andcomparing said expression profile to a reference value; whereby arelative abundance of said nucleic acid sequences allows the detectionof said papillary RCC or oncocytoma.

According to some embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 4, 5, 8-11,45-47, is indicative of the presence of papillary RCC.

According to other embodiments, a relative abundance of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 12, 13,25-27, 41-42, is indicative of the presence of oncocytoma.

The invention further provides a method for distinguishing betweenchromophobe RCC, clear cell RCC, papillary RCC and oncocytoma, themethod comprising: obtaining a biological sample from a subject;determining in said sample an expression profile of nucleic acidsequences selected from the group consisting of SEQ ID NOS: 4, 5, 13-15,20, 21, 25, 34, 35, 41, 42; a fragment thereof and a sequence having atleast 80% identity thereto; and comparing said expression profile to areference value; whereby a relative abundance of said nucleic acidsequences allows the detection of said RCC.

In certain embodiments, the subject is a human.

In certain embodiments, the method is used to determine a course oftreatment of the subject.

According to other embodiments, said biological sample is selected fromthe group consisting of bodily fluid, a cell line and a tissue sample.According to some embodiments, said tissue is a fresh, frozen, fixed,wax-embedded or formalin fixed paraffin-embedded (FFPE) tissue.

According to some embodiments said tissue sample is a kidney tumorsample.

According to some embodiments said tissue sample is selected from thegroup consisting of benign renal tissue sample and malignant renaltissue sample.

According to some embodiments, the method comprises determining theexpression levels of at least one nucleic acid sequence. According tosome embodiments the method further comprising combining one or moreexpression ratios. According to some embodiments, the expression levelsare determined by a method selected from the group consisting of nucleicacid hybridization, nucleic acid amplification, and a combinationthereof. According to some embodiments, the nucleic acid hybridizationis performed using a solid-phase nucleic acid biochip array. Accordingto certain embodiments, the nucleic acid hybridization is performedusing in situ hybridization. According to some embodiments, the in situhybridization method comprises hybridization with a probe. According toother embodiments, the probe comprises a nucleic acid sequence that iscomplementary to a sequence selected from the group consisting of SEQ IDNOS: 1-75 and sequences at least about 80% identical thereto.

According to other embodiments, the nucleic acid amplification method isreal-time PCR (RT-PCR). According to one embodiment, said real-time PCRis quantitative real-time PCR (qRT-PCR).

According to some embodiments, the RT-PCR method comprises forward andreverse primers. According to other embodiments, the forward primercomprises a sequence selected from the group consisting of SEQ ID NOS:82-87, a fragment thereof and a sequence having at least about 80%identity thereto. According to other embodiments, the reverse primercomprises SEQ ID NO: 90, a fragment thereof and a sequence having atleast about 80% identity thereto.

According to some embodiments, the real-time PCR method furthercomprises hybridization with a probe. According to other embodiments,the probe comprises a nucleic acid sequence that is complementary to asequence selected from the group consisting of SEQ ID NOS: 1-75, afragment thereof and sequences at least about 80% identical thereto.

According to other embodiments, the probe comprises a sequence selectedfrom the group consisting of any one of SEQ ID NOS: 76-81, a fragmentthereof and sequences at least about 80% identical thereto.

The invention further provides a kit for renal tumor classification,said kit comprises a probe comprising a nucleic acid sequence that iscomplementary to a sequence selected from the group consisting of SEQ IDNOS: 1-75, a fragment thereof and sequences having at least about 80%identity thereto.

According to some embodiments, said probe comprising a nucleic acidsequence selected from the group consisting of SEQ ID NOS: 76-81, afragment thereof and a sequence at least about 80% identical thereto.

According to some embodiments, the kit further comprises forward andreverse primers. According to some embodiments, the forward primercomprising a sequence selected from the group consisting of SEQ ID NOS:82-87, a fragment thereof and a sequence having at least about 80%identity thereto.

According to other embodiments, the reverse primer comprises SEQ ID NO:90, a fragment thereof and sequences having at least about 80% identitythereto.

These and other embodiments of the present invention will becomeapparent in conjunction with the figures, description and claims thatfollow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a Scatter-plot comparison of oncocytoma and chromophobesamples from the training set. Median normalized fluorescence ofoncocytoma samples (n=21) (Y axis) is plotted against the mediannormalized fluorescence of chromophobe samples (n=13) (X axis). EachmicroRNA is represented by a black cross. Control probes and microRNAsthat did not pass the minimum expression threshold of (median normalizedfluorescence>700 in at least one of the two groups) are shown in grey.Diagonal line shows equal signals (dash-dot line) and 2-fold change ineither direction (dotted lines). MicroRNAs that had a fold changegreater than 4 in either direction and had a p-value lower than 0.0417(the threshold determined for a False Discovery Rate of 0.1 or lower)are highlighted in red circles.

FIG. 2 is a Volcano plot showing the −log 10 (p-value) against the log 2of the ratio of the median expression, for the same data of FIG. 1.MicroRNAs with strong fold-changes have a large absolute value of thelog 2 (ratio). Vertical lines indicate 4-fold change in median signal ineither direction; horizontal line indicates the p-value cutoff of0.0417.

The results are based on microarray analysis. Statistically significantmiRs with p-value smaller than 0.0417 and at least 4-fold change inexpression are marked with circles: hsa-miR-139-5p (SEQ ID NO: 14),hsa-miR-551b (SEQ ID NO: 30), hsa-miR-141 (SEQ ID NO: 32), hsa-miR-200b(SEQ ID NO: 54), hsa-miR-637 (SEQ ID NO: 56), hsa-miR-373* (SEQ ID NO:58) and hsa-miR-200c (SEQ ID NO: 34).

FIGS. 3A-3D demonstrate separation between histological types using asmall set of microRNAs. The training set included oncocytoma samples(“0”, n=21, stars), chromophobe tumors (“H”, n=13, diamonds),conventional cell tumors (“C”, n=17, squares), and papillary tumors(“P”, n=20, circles). Plotting the expression of hsa-miR-221 (SEQ ID NO:25), hsa-miR-31 (SEQ ID NO: 4) and hsa-miR-200c (SEQ ID NO: 34) in eachof the training set samples (FIG. 3A), the four types of samples groupinto areas with distinct ranges of expression. Box-plots (FIGS. 3B, 3Cand 3D) indicate expression levels of microRNAs in the four histologicaltypes (same samples as in FIG. 3A), showing the median value (horizontalline), 25 to 75 percentile (box), extent of data (“whiskers”) andoutliers (crosses). hsa-miR-221 (SEQ ID NO: 25) and hsa-miR-210 (SEQ IDNO: 20) (FIG. 3B) have distinct expression in oncocytomas andchromophobe tumors compared to conventional cell and papillary tumor,with hsa-miR-221 more strongly expressed in oncocytomas and chromophobetumors, and hsa-miR-210 more strongly expressed in conventional cell andpapillary tumors. hsa-miR-139-5p (SEQ ID NO: 14) and hsa-miR-200c (SEQID NO: 34) (FIG. 3C) have distinct expression in oncocytomas compared tochromophobe tumors, with hsa-miR-139-5p more strongly expressed inoncocytomas and hsa-miR-200c more strongly expressed in chromophobetumors. hsa-miR-126 (SEQ ID NO: 41) and hsa-miR-31 (SEQ ID NO: 4) (FIG.3D) have distinct expression in conventional cell tumors compared topapillary tumors, with hsa-miR-126 more strongly expressed inconventional cell tumors and hsa-miR-31 more strongly expressed inpapillary tumors.

FIGS. 4A-4D show classification of kidney tumors using expression levelsof six microRNAs as detected by microarray.

FIG. 4A) Classification proceeds in two steps, following the clusterstructure of the histological types: first, samples are classified intoeither the oncocytoma/chromophobe pair, or the conventional/papillarypair, using expression levels of hsa-miR-210 (SEQ ID NO: 20) andhsa-miR-221 (SEQ ID NO: 25) (FIG. 4B). In the second step, oncocytoma isidentified from chromophobe using expression levels of hsa-miR-200c (SEQID NO: 34) and hsa-miR-139-5p (SEQ ID NO: 14) (FIG. 4C), andconventional cell is identified from papillary using expression levelsof hsa-miR-31 (SEQ ID NO: 4) and hsa-miR-126 (SEQ ID NO: 41).Independent test samples included oncocytoma samples (n=19, stars),chromophobe tumors (n=14, diamonds), conventional cell tumors (n=17,squares), and papillary tumors (n=6, circles). The grey regions indicatethe thresholds for classification for each pair of microRNAs, indicatingin each case the right branch in the binary classification tree (FIG.4A). The 71 samples that were used for training the thresholds are shownin faded symbols in the background.

FIGS. 5A-5C show classification of kidney tumors using expression levelsof six microRNAs as detected by qRT-PCR. FIG. 5A demonstrates Node 1 inthe classification tree (as presented in FIG. 4A)—classification ofoncocytoma (stars) and chromophobe (diamonds) samples from conventionalcell (squares) and papillary (circles) samples using expression levelsof hsa-miR-210 (SEQ ID NO: 20) and hsa-miR-221 (SEQ ID NO: 25). Alogistic regression classifier was used in order to obtain a threshold.The black dots denote samples that got an erroneous classification usingLOOCV (in the entire tree; not specifically in the current node).

FIG. 5B demonstrates Node 2 in the classification tree—classification ofoncocytoma (stars) from chromophobe (diamonds) samples usinghsa-miR-200c (SEQ ID NO: 34) and hsa-miR-139-5p (SEQ ID NO: 14). Alogistic regression classifier was used in order to obtain a threshold.

FIG. 5C demonstrates Node 3 in the classification tree—classification ofconventional cell (squares) from papillary (circles) samples usinghsa-miR-31 (SEQ ID NO: 4) and hsa-miR-126 (SEQ ID NO: 41). A logisticregression classifier was used in order to obtain a threshold. The blackdots denote samples that got an erroneous classification using LOOCV (inthe entire tree; not specifically in the current node).

DETAILED DESCRIPTION OF THE INVENTION

The invention is based on the discovery that specific nucleic acidsequences (SEQ ID NOS: 1-90) can be used for the classification ofkidney tumors. The present invention provides a sensitive, specific andaccurate method which can be used to distinguish between particularsubtypes of kidney tumors.

Renal cell cancer comprise of different subtypes of cancers that differin genetic background, response to surgical and medical therapy andprognosis. The different histological subclasses of RCC are associatedwith the different disease specific survival that range from 24% to 100%at 5 years from surgery. While non conventional types RCC have a lowerpathological stage and reduced portion of metastatic disease, itsresponse to systemic medical therapy is reduced compared to conventionaltype RCC. Various markers have been suggested and used for thisdistinction between histology subtypes, but these show mixed or limitedspecificities, and a significant fraction of samples may be unclassifiedor misclassified. Unclassified RCC comprise of up to 6% of all RCC evenin series from centers of excellence and have the worst clinical outcomeas compared to other subclasses. One can assume that the proportion ofunclassified RCC is higher in centers lacking dedicated pathologistsfocusing in genitourinary malignancies and therefore emphasizes thatneed for additional diagnostic tools for RCC subclassification.

According to the present invention, a new microRNA-based classifier wasdeveloped for determining the specific subtypes of kidney tumors. Theclassifier uses a transparent algorithm and allows a clearinterpretation of the specific biomarkers.

The microRNA-based classifier reached an accuracy of 93% in histologicalclassification of an independent set of 56 test samples. This diagnosticmodel can potentially be used at preoperative and postoperative settingin order to differentiate the 4 major RCC subtypes, and may be a usefulclinical tool for the diagnosis and management of renal tumor cases.

The differentially expressed microRNAs can provide clues to thebiological differences between the subtypes, their diverging oncogeneticprocesses and possible new targets for type specific target therapy. Itwas found that hsa-miR-141 (SEQ ID NO: 32) and hsa-miR-200c (SEQ ID NO:34) are specifically expressed in the chromophobe tumors. hsa-miR-221(SEQ ID NO: 25) and hsa-miR-222 (SEQ ID NO: 26) are strongly expressedin both chromophobe and oncocytoma types. These microRNAs inhibiterythropoietic growth by targeting and down-regulating the KIT receptor.Interestingly, KIT was found to be expressed specifically in oncocytomaand chromophobe subtypes of RCC. Other microRNAs show strong differencesin expression between the subtypes (Table 2), but their involvement inthe oncogenic process is not clear. Some clues or links to other knownpathways may be found through transcription factors that potentiallyregulate these microRNAs (Table 3).

The possibility to distinguish between different subtypes of kidneytumors facilitates providing the patient with the best and most suitabletreatment.

The present invention provides diagnostic assays and methods, bothquantitative and qualitative for detecting, diagnosing, monitoring,staging and prognosticating kidney cancers by comparing levels of thespecific microRNA molecules of the invention. Such levels are preferablymeasured in at least one of biopsies, tumor samples, cells, tissuesand/or bodily fluids. The present invention provides methods fordiagnosing the presence of a specific kidney cancer by analyzing thelevels of said microRNA molecules in biopsies, tumor samples, cells,tissues or bodily fluids.

In the present invention, determining the levels of said microRNAs inbiopsies, tumor samples, cells, tissues or bodily fluid, is particularlyuseful for discriminating between different subtypes of kidney tumors.

All the methods of the present invention may optionally further includemeasuring levels of other cancer markers. Other cancer markers, inaddition to said microRNA molecules, useful in the present inventionwill depend on the cancer being tested and are known to those of skillin the art.

Assay techniques that can be used to determine levels of geneexpression, such as the nucleic acid sequence of the present invention,in a sample derived from a patient are well known to those of skill inthe art. Such assay methods include, but are not limited to,radioimmunoassays, reverse transcriptase PCR (RT-PCR) assays,immunohistochemistry assays, in situ hybridization assays,competitive-binding assays, Northern Blot analyses, ELISA assays,nucleic acid microarrays and biochip analysis.

An arbitrary threshold on the expression level of one or more nucleicacid sequences can be set for assigning a sample or tumor sample to oneof two groups. Alternatively, in a preferred embodiment, expressionlevels of one or more nucleic acid sequences of the invention arecombined by taking ratios of expression levels of two nucleic acidsequences and/or by a method such as logistic regression to define ametric which is then compared to previously measured samples or to athreshold. The threshold for assignment is treated as a parameter, whichcan be used to quantify the confidence with which samples are assignedto each class. The threshold for assignment can be scaled to favorsensitivity or specificity, depending on the clinical scenario. Thecorrelation value to the reference data generates a continuous scorethat can be scaled and provides diagnostic information on the likelihoodthat a samples belongs to a certain class of renal subtype. Inmultivariate analysis, the microRNA signature provides a high level ofprognostic information.

DEFINITIONS

It is to be understood that the terminology used herein is for thepurpose of describing particular embodiments only and is not intended tobe limiting. It must be noted that, as used in the specification and theappended claims, the singular forms “a,” “an” and “the” include pluralreferents unless the context clearly dictates otherwise.

For the recitation of numeric ranges herein, each intervening numberthere between with the same degree of precision is explicitlycontemplated. For example, for the range of 6-9, the numbers 7 and 8 arecontemplated in addition to 6 and 9, and for the range 6.0-7.0, thenumber 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9 and 7.0 areexplicitly contemplated.

Aberrant Proliferation

As used herein, the term “aberrant proliferation” means cellproliferation that deviates from the normal, proper, or expected course.For example, aberrant cell proliferation may include inappropriateproliferation of cells whose DNA or other cellular components havebecome damaged or defective. Aberrant cell proliferation may includecell proliferation whose characteristics are associated with anindication caused by, mediated by, or resulting in inappropriately highlevels of cell division, inappropriately low levels of apoptosis, orboth. Such indications may be characterized, for example, by single ormultiple local abnormal proliferations of cells, groups of cells, ortissue(s), whether cancerous or non-cancerous, benign or malignant.

About

As used herein, the term “about” refers to +1-10%.

Attached

“Attached” or “immobilized” as used herein to refer to a probe and asolid support means that the binding between the probe and the solidsupport is sufficient to be stable under conditions of binding, washing,analysis, and removal. The binding may be covalent or non-covalent.Covalent bonds may be formed directly between the probe and the solidsupport or may be formed by a cross linker or by inclusion of a specificreactive group on either the solid support or the probe or bothmolecules. Non-covalent binding may be one or more of electrostatic,hydrophilic, and hydrophobic interactions. Included in non-covalentbinding is the covalent attachment of a molecule, such as streptavidin,to the support and the non-covalent binding of a biotinylated probe tothe streptavidin. Immobilization may also involve a combination ofcovalent and non-covalent interactions.

Biological Sample

“Biological sample” as used herein means a sample of biological tissueor fluid that comprises nucleic acids. Such samples include, but are notlimited to, tissue or fluid isolated from subjects. Biological samplesmay also include sections of tissues such as biopsy and autopsy samples,FFPE samples, frozen sections taken for histological purposes, blood,blood fraction, plasma, serum, sputum, stool, tears, mucus, hair, skin,urine, effusions, ascitic fluid, amniotic fluid, saliva, cerebrospinalfluid, cervical secretions, vaginal secretions, endometrial secretions,gastrointestinal secretions, bronchial secretions, cell line, tissuesample, or secretions from the breast. A biological sample may beprovided by removing a sample of cells from a subject but can also beaccomplished by using previously isolated cells (e.g., isolated byanother person, at another time, and/or for another purpose), or byperforming the methods described herein in vivo. Archival tissues, suchas those having treatment or outcome history, may also be used.Biological samples also include explants and primary and/or transformedcell cultures derived from animal or human tissues.

Cancer

The term “cancer” is meant to include all types of cancerous growths oroncogenic processes, metastatic tissues or malignantly transformedcells, tissues, or organs, irrespective of histopathologic type or stageof invasiveness. Examples of cancers include but are not limited tosolid tumors and leukemias, including: apudoma, choristoma, branchioma,malignant carcinoid syndrome, carcinoid heart disease, carcinoma (e.g.,Walker, basal cell, basosquamous, Brown-Pearce, ductal, Ehrlich tumor,clear cell RCC, papillary RCC and chromophobe RCC, non-small cell lung(e.g., lung squamous cell carcinoma, lung adenocarcinoma and lungundifferentiated large cell carcinoma), oat cell, papillary,bronchiolar, bronchogenic, squamous cell, and transitional cell),histiocytic disorders, leukemia (e.g., B cell, mixed cell, null cell, Tcell, T-cell chronic, HTLV-II-associated, lymphocytic acute, lymphocyticchronic, mast cell, and myeloid), histiocytosis malignant, Hodgkindisease, immunoproliferative small, non-Hodgkin lymphoma, plasmacytoma,reticuloendotheliosis, melanoma, chondroblastoma, chondroma,chondrosarcoma, fibroma, fibrosarcoma, giant cell tumors, histiocytoma,lipoma, liposarcoma, mesothelioma, myxoma, myxosarcoma, osteoma,osteosarcoma, Ewing sarcoma, synovioma, adenofibroma, adenolymphoma,carcinosarcoma, chordoma, craniopharyngioma, dysgerminoma, hamartoma,mesenchymoma, mesonephroma, myosarcoma, ameloblastoma, cementoma,odontoma, teratoma, thymoma, trophoblastic tumor, adeno-carcinoma,adenoma, cholangioma, cholesteatoma, cylindroma, cystadenocarcinoma,cystadenoma, granulosa cell tumor, gynandroblastoma, hepatoma,hidradenoma, islet cell tumor, Leydig cell tumor, papilloma, Sertolicell tumor, theca cell tumor, leiomyoma, leiomyosarcoma, myoblastoma,myosarcoma, rhabdomyoma, rhabdomyosarcoma, ependymoma, ganglioneuroma,glioma, medulloblastoma, meningioma, neurilemmoma, neuroblastoma,neuroepithelioma, neurofibroma, neuroma, paraganglioma, paragangliomanonchromaffin, angiokeratoma, angiolymphoid hyperplasia witheosinophilia, angioma sclerosing, angiomatosis, glomangioma,hemangioendothelioma, hemangioma, hemangiopericytoma, hemangiosarcoma,lymphangioma, lymphangiomyoma, lymphangiosarcoma, pinealoma,carcinosarcoma, chondrosarcoma, cystosarcoma, phyllodes, fibrosarcoma,hemangiosarcoma, leimyosarcoma, leukosarcoma, liposarcoma,lymphangiosarcoma, myosarcoma, myxosarcoma, ovarian carcinoma,rhabdomyosarcoma, sarcoma (e.g., Ewing, experimental, Kaposi, and mastcell), neurofibromatosis, and cervical dysplasia, and other conditionsin which cells have become immortalized or transformed.

Classification

The term classification refers to a procedure and/or algorithm in whichindividual items are placed into groups or classes based on quantitativeinformation on one or more characteristics inherent in the items(referred to as traits, variables, characters, features, etc) and basedon a statistical model and/or a training set of previously labeleditems.

Complement

“Complement” or “complementary” as used herein to refer to a nucleicacid may mean Watson-Crick (e.g., A-T/U and C-G) or Hoogsteen basepairing between nucleotides or nucleotide analogs of nucleic acidmolecules. A full complement or fully complementary means 100%complementary base pairing between nucleotides or nucleotide analogs ofnucleic acid molecules.

C_(T)

C_(T) signals represent the first cycle of PCR where amplificationcrosses a threshold (cycle threshold) of fluorescence. Accordingly, lowvalues of C_(T) represent high abundance or expression levels of themicroRNA.

In some embodiments the PCR C_(T) signal is normalized such that thenormalized C_(T) remains inversed from the expression level. In otherembodiments the PCR C_(T) signal may be normalized and then invertedsuch that low normalized-inverted C_(T) represents low abundance orexpression levels of the microRNA.

Data Processing Routine

As used herein, a “data processing routine” refers to a process that canbe embodied in software that determines the biological significance ofacquired data (i.e., the ultimate results of an assay or analysis). Forexample, the data processing routine can make determination of tissue oforigin based upon the data collected. In the systems and methods herein,the data processing routine can also control the data collection routinebased upon the results determined. The data processing routine and thedata collection routines can be integrated and provide feedback tooperate the data acquisition, and hence provide assay-based judgingmethods.

Data Set

As use herein, the term “data set” refers to numerical values obtainedfrom the analysis. These numerical values associated with analysis maybe values such as peak height and area under the curve.

Data Structure

As used herein the term “data structure” refers to a combination of twoor more data sets, applying one or more mathematical manipulations toone or more data sets to obtain one or more new data sets, ormanipulating two or more data sets into a form that provides a visualillustration of the data in a new way. An example of a data structureprepared from manipulation of two or more data sets would be ahierarchical cluster.

Detection

“Detection” means detecting the presence of a component in a sample.Detection also means detecting the absence of a component. Detectionalso means determining the level of a component, either quantitativelyor qualitatively.

Differential Expression

“Differential expression” means qualitative or quantitative differencesin the temporal and/or spatial gene expression patterns within and amongcells and tissue. Thus, a differentially expressed gene mayqualitatively have its expression altered, including an activation orinactivation, in, e.g., normal versus diseased tissue. Genes may beturned on or turned off in a particular state, relative to another statethus permitting comparison of two or more states. A qualitativelyregulated gene may exhibit an expression pattern within a state or celltype which may be detectable by standard techniques. Some genes may beexpressed in one state or cell type, but not in both. Alternatively, thedifference in expression may be quantitative, e.g., in that expressionis modulated, up-regulated, resulting in an increased amount oftranscript, or down-regulated, resulting in a decreased amount oftranscript. The degree to which expression differs needs only be largeenough to quantify via standard characterization techniques such asexpression arrays, quantitative reverse transcriptase PCR, Northern blotanalysis, real-time PCR, in situ hybridization and RNase protection.

Expression Profile

The term “expression profile” is used broadly to include a genomicexpression profile, e.g., an expression profile of microRNAs. Profilesmay be generated by any convenient means for determining a level of anucleic acid sequence e.g. quantitative hybridization of microRNA,labeled microRNA, amplified microRNA, cDNA, etc., quantitative PCR,ELISA for quantitation, and the like, and allow the analysis ofdifferential gene expression between two samples. A subject or patienttumor sample, e.g., cells or collections thereof, e.g., tissues, isassayed. Samples are collected by any convenient method, as known in theart. Nucleic acid sequences of interest are nucleic acid sequences thatare found to be predictive, including the nucleic acid sequencesprovided above, where the expression profile may include expression datafor 5, 10, 20, 25, 50, 100 or more of, including all of the listednucleic acid sequences. According to some embodiments, the term“expression profile” means measuring the abundance of the nucleic acidsequences in the measured samples.

Expression Ratio

“Expression ratio” as used herein refers to relative expression levelsof two or more nucleic acids as determined by detecting the relativeexpression levels of the corresponding nucleic acids in a biologicalsample.

FDR

When performing multiple statistical tests, for example in comparing thesignal between two groups in multiple data features, there is anincreasingly high probability of obtaining false positive results, byrandom differences between the groups that can reach levels that wouldotherwise be considered as statistically significant. In order to limitthe proportion of such false discoveries, statistical significance isdefined only for data features in which the differences reached ap-value (by two-sided t-test) below a threshold, which is dependent onthe number of tests performed and the distribution of p-values obtainedin these tests.

Fragment

“Fragment” is used herein to indicate a non-full length part of anucleic acid or polypeptide. Thus, a fragment is itself also a nucleicacid or polypeptide, respectively.

Gene

“Gene” as used herein may be a natural (e.g., genomic) or synthetic genecomprising transcriptional and/or translational regulatory sequencesand/or a coding region and/or non-translated sequences (e.g., introns,5′- and 3′-untranslated sequences). The coding region of a gene may be anucleotide sequence coding for an amino acid sequence or a functionalRNA, such as tRNA, rRNA, catalytic RNA, siRNA, miRNA or antisense RNA. Agene may also be an mRNA or cDNA corresponding to the coding regions(e.g., exons and miRNA) optionally comprising 5′- or 3′-untranslatedsequences linked thereto. A gene may also be an amplified nucleic acidmolecule produced in vitro comprising all or a part of the coding regionand/or 5′- or 3′-untranslated sequences linked thereto.

Groove Binder/Minor Groove Binder (MGB)

“Groove binder” and/or “minor groove binder” may be used interchangeablyand refer to small molecules that fit into the minor groove ofdouble-stranded DNA, typically in a sequence-specific manner. Minorgroove binders may be long, flat molecules that can adopt acrescent-like shape and thus, fit snugly into the minor groove of adouble helix, often displacing water. Minor groove binding molecules maytypically comprise several aromatic rings connected by bonds withtorsional freedom such as furan, benzene, or pyrrole rings. Minor groovebinders may be antibiotics such as netropsin, distamycin, berenil,pentamidine and other aromatic diamidines, Hoechst 33258, SN 6999,aureolic anti-tumor drugs such as chromomycin and mithramycin, CC-1065,dihydrocyclopyrroloindole tripeptide (DPI₃),1,2-dihydro-(3H)-pyrrolo[3,2-e]indole-7-carboxylate (CDPI₃), and relatedcompounds and analogues, including those described in Nucleic Acids inChemistry and Biology, 2d ed., Blackburn and Gait, eds., OxfordUniversity Press, 1996, and PCT Published Application No. WO 03/078450,the contents of which are incorporated herein by reference. A minorgroove binder may be a component of a primer, a probe, a hybridizationtag complement, or combinations thereof. Minor groove binders mayincrease the T_(m) of the primer or a probe to which they are attached,allowing such primers or probes to effectively hybridize at highertemperatures.

Identity

“Identical” or “identity” as used herein in the context of two or morenucleic acids or polypeptide sequences mean that the sequences have aspecified percentage of residues that are the same over a specifiedregion. The percentage may be calculated by optimally aligning the twosequences, comparing the two sequences over the specified region,determining the number of positions at which the identical residueoccurs in both sequences to yield the number of matched positions,dividing the number of matched positions by the total number ofpositions in the specified region, and multiplying the result by 100 toyield the percentage of sequence identity. In cases where the twosequences are of different lengths or the alignment produces one or morestaggered ends and the specified region of comparison includes only asingle sequence, the residues of single sequence are included in thedenominator but not the numerator of the calculation. When comparing DNAand RNA sequences, thymine (T) and uracil (U) may be consideredequivalent. Identity may be performed manually or by using a computersequence algorithm such as BLAST or BLAST 2.0.

In Situ Detection

“In situ detection” as used herein means the detection of expression orexpression levels in the original site hereby meaning in a tissue samplesuch as biopsy.

Label

“Label” as used herein means a composition detectable by spectroscopic,photochemical, biochemical, immunochemical, chemical, or other physicalmeans. For example, useful labels include P³², fluorescent dyes,electron-dense reagents, enzymes (e.g., as commonly used in an ELISA),biotin, digoxigenin, or haptens and other entities which can be madedetectable. A label may be incorporated into nucleic acids and proteinsat any position.

Logistic Regression

Logistic regression is part of a category of statistical models calledgeneralized linear models. Logistic regression allows one to predict adiscrete outcome, such as group membership, from a set of variables thatmay be continuous, discrete, dichotomous, or a mix of any of these. Thedependent or response variable can be dichotomous, for example, one oftwo possible types of cancer. Logistic regression models the natural logof the odds ratio, i.e. the ratio of the probability of belonging to thefirst group (P) over the probability of belonging to the second group(1−P), as a linear combination of the different expression levels (inlog-space). The logistic regression output can be used as a classifierby prescribing that a case or sample will be classified into the firsttype is P is greater than 0.5 or 50%. Alternatively, the calculatedprobability P can be used as a variable in other contexts such as a 1Dor 2D threshold classifier.

1D/2D Threshold Classifier

“1D/2D threshold classifier” used herein may mean an algorithm forclassifying a case or sample such as a cancer sample into one of twopossible types such as two types of cancer. For a 1D thresholdclassifier, the decision is based on one variable and one predeterminedthreshold value; the sample is assigned to one class if the variableexceeds the threshold and to the other class if the variable is lessthan the threshold. A 2D threshold classifier is an algorithm forclassifying into one of two types based on the values of two variables.A threshold may be calculated as a function (usually a continuous oreven a monotonic function) of the first variable; the decision is thenreached by comparing the second variable to the calculated threshold,similar to the 1D threshold classifier.

Nucleic Acid

“Nucleic acid” or “oligonucleotide” or “polynucleotide”, as used hereinmeans at least two nucleotides covalently linked together. The depictionof a single strand also defines the sequence of the complementarystrand. Thus, a nucleic acid also encompasses the complementary strandof a depicted single strand. Many variants of a nucleic acid may be usedfor the same purpose as a given nucleic acid. Thus, a nucleic acid alsoencompasses substantially identical nucleic acids and complementsthereof. A single strand provides a probe that may hybridize to a targetsequence under stringent hybridization conditions. Thus, a nucleic acidalso encompasses a probe that hybridizes under stringent hybridizationconditions.

Nucleic acids may be single stranded or double stranded, or may containportions of both double stranded and single stranded sequences. Thenucleic acid may be DNA, both genomic and cDNA, RNA, or a hybrid, wherethe nucleic acid may contain combinations of deoxyribo- andribo-nucleotides, and combinations of bases including uracil, adenine,thymine, cytosine, guanine, inosine, xanthine hypoxanthine, isocytosineand isoguanine. Nucleic acids may be obtained by chemical synthesismethods or by recombinant methods.

A nucleic acid will generally contain phosphodiester bonds, althoughnucleic acid analogs may be included that may have at least onedifferent linkage, e.g., phosphoramidate, phosphorothioate,phosphorodithioate, or O-methylphosphoroamidite linkages and peptidenucleic acid backbones and linkages. Other analog nucleic acids includethose with positive backbones; non-ionic backbones, and non-ribosebackbones, including those described in U.S. Pat. Nos. 5,235,033 and5,034,506, which are incorporated herein by reference. Nucleic acidscontaining one or more non-naturally occurring or modified nucleotidesare also included within one definition of nucleic acids. The modifiednucleotide analog may be located for example at the 5′-end and/or the3′-end of the nucleic acid molecule. Representative examples ofnucleotide analogs may be selected from sugar- or backbone-modifiedribonucleotides. It should be noted, however, that alsonucleobase-modified ribonucleotides, i.e. ribonucleotides, containing anon-naturally occurring nucleobase instead of a naturally occurringnucleobase such as uridines or cytidines modified at the 5-position,e.g. 5-(2-amino) propyl uridine, 5-bromo uridine; adenosines andguanosines modified at the 8-position, e.g. 8-bromo guanosine; deazanucleotides, e.g. 7-deaza-adenosine; 0- and N-alkylated nucleotides,e.g. N6-methyl adenosine are suitable. The 2′-OH-group may be replacedby a group selected from H, OR, R, halo, SH, SR, NH2, NHR, NR2 or CN,wherein R is C1-C6 alkyl, alkenyl or alkynyl and halo is F, Cl, Br or I.Modified nucleotides also include nucleotides conjugated withcholesterol through, e.g., a hydroxyprolinol linkage as described inKrutzfeldt et al., Nature 438:685-689 (2005), Soutschek et al., Nature432:173-178 (2004), and U.S. Patent Publication No. 20050107325, whichare incorporated herein by reference. Additional modified nucleotidesand nucleic acids are described in U.S. Patent Publication No.20050182005, which is incorporated herein by reference. Modifications ofthe ribose-phosphate backbone may be done for a variety of reasons,e.g., to increase the stability and half-life of such molecules inphysiological environments, to enhance diffusion across cell membranes,or as probes on a biochip. The backbone modification may also enhanceresistance to degradation, such as in the harsh endocytic environment ofcells. The backbone modification may also reduce nucleic acid clearanceby hepatocytes, such as in the liver and kidney. Mixtures of naturallyoccurring nucleic acids and analogs may be made; alternatively, mixturesof different nucleic acid analogs, and mixtures of naturally occurringnucleic acids and analogs may be made.

Probe

“Probe” as used herein means an oligonucleotide capable of binding to atarget nucleic acid of complementary sequence through one or more typesof chemical bonds, usually through complementary base pairing, usuallythrough hydrogen bond formation. Probes may bind target sequenceslacking complete complementarity with the probe sequence depending uponthe stringency of the hybridization conditions. There may be any numberof base pair mismatches which will interfere with hybridization betweenthe target sequence and the single stranded nucleic acids describedherein. However, if the number of mutations is so great that nohybridization can occur under even the least stringent of hybridizationconditions, the sequence is not a complementary target sequence. A probemay be single stranded or partially single and partially doublestranded. The strandedness of the probe is dictated by the structure,composition, and properties of the target sequence. Probes may bedirectly labeled or indirectly labeled such as with biotin to which astreptavidin complex may later bind.

Reference Value

As used herein the term “reference value” means a value thatstatistically correlates to a particular outcome when compared to anassay result. In preferred embodiments the reference value is determinedfrom statistical analysis of studies that compare microRNA expressionwith known clinical outcomes.

Sensitivity

“sensitivity” used herein may mean a statistical measure of how well abinary classification test correctly identifies a condition, for examplehow frequently it correctly classifies a cancer into the correct typeout of two possible types. The sensitivity for class A is the proportionof cases that are determined to belong to class “A” by the test out ofthe cases that are in class “A”, as determined by some absolute or goldstandard.

Specificity

“Specificity” used herein may mean a statistical measure of how well abinary classification test correctly identifies a condition, for examplehow frequently it correctly classifies a cancer into the correct typeout of two possible types. The specificity for class A is the proportionof cases that are determined to belong to class “not A” by the test outof the cases that are in class “not A”, as determined by some absoluteor gold standard.

Stage of Cancer

As used herein, the term “stage of cancer” refers to a numericalmeasurement of the level of advancement of a cancer. Criteria used todetermine the stage of a cancer include, but are not limited to, thesize of the tumor, whether the tumor has spread to other parts of thebody and where the cancer has spread (e.g., within the same organ orregion of the body or to another organ).

Stringent Hybridization Conditions

“Stringent hybridization conditions” as used herein mean conditionsunder which a first nucleic acid sequence (e.g., probe) will hybridizeto a second nucleic acid sequence (e.g., target), such as in a complexmixture of nucleic acids. Stringent conditions are sequence-dependentand will be different in different circumstances. Stringent conditionsmay be selected to be about 5-10° C. lower than the thermal meltingpoint (T_(m)) for the specific sequence at a defined ionic strength pH.The T_(m) may be the temperature (under defined ionic strength, pH, andnucleic concentration) at which 50% of the probes complementary to thetarget hybridize to the target sequence at equilibrium (as the targetsequences are present in excess, at T_(m), 50% of the probes areoccupied at equilibrium). Stringent conditions may be those in which thesalt concentration is less than about 1.0 M sodium ion, such as about0.01-1.0 M sodium ion concentration (or other salts) at pH 7.0 to 8.3and the temperature is at least about 30° C. for short probes (e.g.,about 10-50 nucleotides) and at least about 60° C. for long probes(e.g., greater than about 50 nucleotides). Stringent conditions may alsobe achieved with the addition of destabilizing agents such as formamide.For selective or specific hybridization, a positive signal may be atleast 2 to 10 times background hybridization. Exemplary stringenthybridization conditions include the following: 50% formamide, 5×SSC,and 1% SDS, incubating at 42° C., or, 5×SSC, 1% SDS, incubating at 65°C., with wash in 0.2×SSC, and 0.1% SDS at 65° C.

Substantially Complementary

“Substantially complementary” as used herein means that a first sequenceis at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98% or 99%identical to the complement of a second sequence over a region of 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35,40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 or more nucleotides,or that the two sequences hybridize under stringent hybridizationconditions.

Substantially Identical

“Substantially identical” as used herein means that a first and a secondsequence are at least 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, 98%or 99% identical over a region of 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75,80, 85, 90, 95, 100 or more nucleotides or amino acids, or with respectto nucleic acids, if the first sequence is substantially complementaryto the complement of the second sequence.

Subtype of Cancer

As used herein, the term “subtype of cancer” refers to different typesof cancer that effect the same organ (e.g., spindle cell, cystic andcollecting duct carcinomas of the kidney).

Subject

As used herein, the term “subject” refers to a mammal, including bothhuman and other mammals. The methods of the present invention arepreferably applied to human subjects.

Target Nucleic Acid

“Target nucleic acid” as used herein means a nucleic acid or variantthereof that may be bound by another nucleic acid. A target nucleic acidmay be a DNA sequence. The target nucleic acid may be RNA. The targetnucleic acid may comprise a mRNA, tRNA, shRNA, siRNA or Piwi-interactingRNA, or a pri-miRNA, pre-miRNA, miRNA, or anti-miRNA.

The target nucleic acid may comprise a target miRNA binding site or avariant thereof. One or more probes may bind the target nucleic acid.The target binding site may comprise 5-100 or 10-60 nucleotides. Thetarget binding site may comprise a total of 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,30-40, 40-50, 50-60, 61, 62 or 63 nucleotides. The target site sequencemay comprise at least 5 nucleotides of the sequence of a target miRNAbinding site disclosed in U.S. patent application Ser. Nos. 11/384,049,11/418,870 or 11/429,720, the contents of which are incorporated herein.

Threshold Expression Profile

As used herein, the phrase “threshold expression profile” refers to acriterion expression profile to which measured values are compared inorder to classify a tumor.

Tissue Sample

As used herein, a tissue sample is tissue obtained from a tissue biopsyusing methods well known to those of ordinary skill in the relatedmedical arts. The phrase “suspected of being cancerous” as used hereinmeans a cancer tissue sample believed by one of ordinary skill in themedical arts to contain cancerous cells. Methods for obtaining thesample from the biopsy include gross apportioning of a mass,microdissection, laser-based microdissection, or other art-knowncell-separation methods.

Tumor

“Tumor” as used herein, refers to all neoplastic cell growth andproliferation, whether malignant or benign, and all pre-cancerous andcancerous cells and tissues.

Variant

“Variant” as used herein referring to a nucleic acid means (i) a portionof a referenced nucleotide sequence; (ii) the complement of a referencednucleotide sequence or portion thereof; (iii) a nucleic acid that issubstantially identical to a referenced nucleic acid or the complementthereof; or (iv) a nucleic acid that hybridizes under stringentconditions to the referenced nucleic acid, complement thereof, or asequence substantially identical thereto.

Wild Type

As used herein, the term “wild type” sequence refers to a coding, anon-coding or an interface sequence which is an allelic form of sequencethat performs the natural or normal function for that sequence. Wildtype sequences include multiple allelic forms of a cognate sequence, forexample, multiple alleles of a wild type sequence may encode silent orconservative changes to the protein sequence that a coding sequenceencodes.

The present invention employs miRNAs for the identification,classification and diagnosis of specific cancers and the identificationof their tissues of origin.

microRNA Processing

A gene coding for microRNA (miRNA) may be transcribed leading toproduction of a miRNA primary transcript known as the pri-miRNA. Thepri-miRNA may comprise a hairpin with a stem and loop structure. Thestem of the hairpin may comprise mismatched bases. The pri-miRNA maycomprise several hairpins in a polycistronic structure.

The hairpin structure of the pri-miRNA may be recognized by Drosha,which is an RNase III endonuclease. Drosha may recognize terminal loopsin the pri-miRNA and cleave approximately two helical turns into thestem to produce a 60-70 nt precursor known as the pre-miRNA. Drosha maycleave the pri-miRNA with a staggered cut typical of RNase IIIendonucleases yielding a pre-miRNA stem loop with a 5′ phosphate and ˜2nucleotide 3′ overhang. Approximately one helical turn of stem (˜10nucleotides) extending beyond the Drosha cleavage site may be essentialfor efficient processing. The pre-miRNA may then be actively transportedfrom the nucleus to the cytoplasm by Ran-GTP and the export receptorEx-portin-5.

The pre-miRNA may be recognized by Dicer, which is also an RNase IIIendonuclease. Dicer may recognize the double-stranded stem of thepre-miRNA. Dicer may also off the terminal loop two helical turns awayfrom the base of the stem loop leaving an additional 5′ phosphate and ˜2nucleotide 3′ overhang. The resulting siRNA-like duplex, which maycomprise mismatches, comprises the mature miRNA and a similar-sizedfragment known as the miRNA*. The miRNA and miRNA* may be derived fromopposing arms of the pri-miRNA and pre-miRNA. MiRNA* sequences may befound in libraries of cloned miRNAs but typically at lower frequencythan the miRNAs.

Although initially present as a double-stranded species with miRNA*, themiRNA may eventually become incorporated as a single-stranded RNA into aribonucleoprotein complex known as the RNA-induced silencing complex(RISC). Various proteins can form the RISC, which can lead tovariability in specificity for miRNA/miRNA* duplexes, binding site ofthe target gene, activity of miRNA (repress or activate), and whichstrand of the miRNA/miRNA* duplex is loaded in to the RISC.

When the miRNA strand of the miRNA:miRNA* duplex is loaded into theRISC, the miRNA* may be removed and degraded. The strand of themiRNA:miRNA* duplex that is loaded into the RISC may be the strand whose5′ end is less tightly paired. In cases where both ends of themiRNA:miRNA* have roughly equivalent 5′ pairing, both miRNA and miRNA*may have gene silencing activity.

The RISC may identify target nucleic acids based on high levels ofcomplementarity between the miRNA and the mRNA, especially bynucleotides 2-7 of the miRNA. Only one case has been reported in animalswhere the interaction between the miRNA and its target was along theentire length of the miRNA. This was shown for mir-196 and Hox B8 and itwas further shown that mir-196 mediates the cleavage of the Hox B8 mRNA(Yekta et al 2004, Science 304-594). Otherwise, such interactions areknown only in plants (Bartel & Bartel 2003, Plant Physiol 132-709).

A number of studies have looked at the base-pairing requirement betweenmiRNA and its mRNA target for achieving efficient inhibition oftranslation (reviewed by Bartel 2004, Cell 116-281). In mammalian cells,the first 8 nucleotides of the miRNA may be important (Doench & Sharp2004 GenesDev 2004-504). However, other parts of the microRNA may alsoparticipate in mRNA binding. Moreover, sufficient base pairing at the 3′can compensate for insufficient pairing at the 5′ (Brennecke et al, 2005PLoS 3-e85). Computation studies, analyzing miRNA binding on wholegenomes have suggested a specific role for bases 2-7 at the 5′ of themiRNA in target binding but the role of the first nucleotide, foundusually to be “A” was also recognized (Lewis et at 2005 Cell 120-15).Similarly, nucleotides 1-7 or 2-8 were used to identify and validatetargets by Krek et al (2005, Nat Genet 37-495).

The target sites in the mRNA may be in the 5′ UTR, the 3′ UTR or in thecoding region. Interestingly, multiple miRNAs may regulate the same mRNAtarget by recognizing the same or multiple sites. The presence ofmultiple miRNA binding sites in most genetically identified targets mayindicate that the cooperative action of multiple RISCs provides the mostefficient translational inhibition.

miRNAs may direct the RISC to downregulate gene expression by either oftwo mechanisms: mRNA cleavage or translational repression. The miRNA mayspecify cleavage of the mRNA if the mRNA has a certain degree ofcomplementarity to the miRNA. When a miRNA guides cleavage, the cut maybe between the nucleotides pairing to residues 10 and 11 of the miRNA.Alternatively, the miRNA may repress translation if the miRNA does nothave the requisite degree of complementarity to the miRNA. Translationalrepression may be more prevalent in animals since animals may have alower degree of complementarity between the miRNA and binding site.

It should be noted that there may be variability in the 5′ and 3′ endsof any pair of miRNA and miRNA*. This variability may be due tovariability in the enzymatic processing of Drosha and Dicer with respectto the site of cleavage. Variability at the 5′ and 3′ ends of miRNA andmiRNA* may also be due to mismatches in the stem structures of thepri-miRNA and pre-miRNA. The mismatches of the stem strands may lead toa population of different hairpin structures. Variability in the stemstructures may also lead to variability in the products of cleavage byDrosha and Dicer.

Nucleic Acids

Nucleic acids are provided herein. The nucleic acids comprise thesequences of SEQ ID NOS: 1-90 or variants thereof. The variant may be acomplement of the referenced nucleotide sequence. The variant may alsobe a nucleotide sequence that is substantially identical to thereferenced nucleotide sequence or the complement thereof. The variantmay also be a nucleotide sequence which hybridizes under stringentconditions to the referenced nucleotide sequence, complements thereof,or nucleotide sequences substantially identical thereto.

The nucleic acid may have a length of from about 10 to about 250nucleotides. The nucleic acid may have a length of at least 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,35, 40, 45, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200 or 250nucleotides. The nucleic acid may be synthesized or expressed in a cell(in vitro or in vivo) using a synthetic gene described herein. Thenucleic acid may be synthesized as a single strand molecule andhybridized to a substantially complementary nucleic acid to form aduplex. The nucleic acid may be introduced to a cell, tissue or organ ina single- or double-stranded form or capable of being expressed by asynthetic gene using methods well known to those skilled in the art,including as described in U.S. Pat. No. 6,506,559 which is incorporatedby reference.

TABLE 1 The nucleic acids of the invention (miRs and hairpins) miR SEQHairpin SEQ ID miR Name ID NO: NO: hsa-miR-194 1 2, 3 hsa-miR-31 4  5hsa-miR-192 6  7 hsa-miR-204 8  9 hsa-mir-21* 10 11 hsa-miR-221* 12 13hsa-miR-139-5p 14 15 hsa-miR-146b-5p 16 17 hsa-miR-10a* 18 19hsa-miR-210 20 21 hsa-miR-155 22 23 hsa-miR-455-3p 24 49 hsa-miR-221 2513 hsa-miR-222 26 27 hsa-miR-140-5p 28 29 hsa-miR-551b 30 31 hsa-miR-14132 33 hsa-miR-200c 34 35 hsa-miR-182 36 37 MID-00536 38 39, 40hsa-miR-126 41 42 hsa-miR-187 43 44 hsa-miR-146a 45 46 hsa-miR-21 47 11hsa-miR-10a 48 19 hsa-miR-138 50 51 hsa-miR-150* 52 53 hsa-miR-200b 5455 hsa-miR-637 56 57 hsa-miR-373* 58 59 hsa-miR-371-5p 60 61 hsa-miR-55762 63 hsa-miR-193b 64 65 hsa-miR-365 66 67 hsa-miR-30b 68 69hsa-miR-196b 70 71 hsa-miR-200a 72 73 hsa-miR-483-5p 74 75 miR name: isthe miRBase registry name (release 10) MID-00536 is not presented in themiRBase registry. It was cloned in Rosetta Genomics.

Nucleic Acid Complexes

The nucleic acid may further comprise one or more of the following: apeptide, a protein, a RNA-DNA hybrid, an antibody, an antibody fragment,a Fab fragment, and an aptamer.

Pri-miRNA

The nucleic acid may comprise a sequence of a pri-miRNA or a variantthereof. The pri-miRNA sequence may comprise from 45-30,000, 50-25,000,100-20,000, 1,000-1,500 or 80-100 nucleotides. The sequence of thepri-miRNA may comprise a pre-miRNA, miRNA and miRNA*, as set forthherein, and variants thereof. The sequence of the pri-miRNA may compriseany of the sequences of SEQ ID NOS: 1-75 or variants thereof.

The pri-miRNA may comprise a hairpin structure. The hairpin may comprisea first and a second nucleic acid sequence that are substantiallycomplimentary. The first and second nucleic acid sequence may be from37-50 nucleotides. The first and second nucleic acid sequence may beseparated by a third sequence of from 8-12 nucleotides. The hairpinstructure may have a free energy of less than −25 Kcal/mole ascalculated by the Vienna algorithm with default parameters, as describedin Hofacker et al., Monatshefte f Chemie 125: 167-188 (1994), thecontents of which are incorporated herein by reference. The hairpin maycomprise a terminal loop of 4-20, 8-12 or 10 nucleotides. The pri-miRNAmay comprise at least 19% adenosine nucleotides, at least 16% cytosinenucleotides, at least 23% thymine nucleotides and at least 19% guaninenucleotides.

Pre-miRNA

The nucleic acid may also comprise a sequence of a pre-miRNA or avariant thereof. The pre-miRNA sequence may comprise from 45-90, 60-80or 60-70 nucleotides. The sequence of the pre-miRNA may comprise a miRNAand a miRNA* as set forth herein. The sequence of the pre-miRNA may alsobe that of a pri-miRNA excluding from 0-160 nucleotides from the 5′ and3′ ends of the pri-miRNA. The sequence of the pre-miRNA may comprise thesequence of SEQ ID NOS: 1-75 or variants thereof.

miRNA

The nucleic acid may also comprise a sequence of a miRNA (includingmiRNA*) or a variant thereof. The miRNA sequence may comprise from13-33, 18-24 or 21-23 nucleotides. The miRNA may also comprise a totalof at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,39 or 40 nucleotides. The sequence of the miRNA may be the first 13-33nucleotides of the pre-miRNA. The sequence of the miRNA may also be thelast 13-33 nucleotides of the pre-miRNA. The sequence of the miRNA maycomprise the sequence of SEQ ID NOS: 1, 4, 6, 8, 10, 12, 14, 16, 18, 20,22, 24, 25, 26, 28, 30, 32, 34, 36, 38, 41, 43, 45, 47, 48, 50, 52, 54,56, 58, 60, 62, 64, 66, 68, 70, 72, and 74, or variants thereof.

Probes

A probe is also provided comprising a nucleic acid described herein.Probes may be used for screening and diagnostic methods, as outlinedbelow. The probe may be attached or immobilized to a solid substrate,such as a biochip.

The probe may have a length of from 8 to 500, 10 to 100 or 20 to 60nucleotides. The probe may also have a length of at least 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, 220,240, 260, 280 or 300 nucleotides. The probe may further comprise alinker sequence of from 10-60 nucleotides.

Test Probe

The probe may be a test probe. The test probe may comprise a nucleicacid sequence that is complementary to a miRNA, a miRNA*, a pre-miRNA,or a pri-miRNA. The sequence of the test probe may be selected from SEQID NOS: 76-81; or variants thereof.

Linker Sequences

The probe may further comprise a linker. The linker may be 10-60nucleotides in length.

The linker may be 20-27 nucleotides in length. The linker may be ofsufficient length to allow the probe to be a total length of 45-60nucleotides. The linker may not be capable of forming a stable secondarystructure, or may not be capable of folding on itself, or may not becapable of folding on a non-linker portion of a nucleic acid containedin the probe. The sequence of the linker may not appear in the genome ofthe animal from which the probe non-linker nucleic acid is derived.

Reverse Transcription

Target sequences of a cDNA may be generated by reverse transcription ofthe target RNA. Methods for generating cDNA may be reverse transcribingpolyadenylated RNA or alternatively, RNA with a ligated adaptorsequence.

Reverse Transcription Using Adaptor Sequence Ligated to RNA

The RNA may be ligated to an adapter sequence prior to reversetranscription. A ligation reaction may be performed by T4 RNA ligase toligate an adaptor sequence at the 3′ end of the RNA. Reversetranscription (RT) reaction may then be performed using a primercomprising a sequence that is complementary to the 3′ end of the adaptorsequence.

Reverse Transcription Using Polyadenylated Sequence Ligated to RNA

Polyadenylated RNA may be used in a reverse transcription (RT) reactionusing a poly(T) primer comprising a 5′ adaptor sequence. The poly(T)sequence may comprise 8, 9, 10, 11, 12, 13, or 14 consecutive thymines.

RT-PCR of RNA

The reverse transcript of the RNA may be amplified by real time PCR,using a specific forward primer comprising at least 15 nucleic acidscomplementary to the target nucleic acid and a 5′ tail sequence; areverse primer that is complementary to the 3′ end of the adaptorsequence; and a probe comprising at least 8 nucleic acids complementaryto the target nucleic acid. The probe may be partially complementary tothe 5′ end of the adaptor sequence.

PCR of Target Nucleic Acids

Methods of amplifying target nucleic acids are described herein. Theamplification may be by a method comprising PCR. The first cycles of thePCR reaction may have an annealing temp of 56° C., 57° C., 58° C., 59°C., or 60° C. The first cycles may comprise 1-10 cycles. The remainingcycles of the PCR reaction may be 60° C. The remaining cycles maycomprise 2-40 cycles. The annealing temperature may cause the PCR to bemore sensitive. The PCR may generate longer products that can serve ashigher stringency PCR templates.

Forward Primer

The PCR reaction may comprise a forward primer. The forward primer maycomprise 15, 16, 17, 18, 19, 20, or 21 nucleotides identical to thetarget nucleic acid.

The 3′ end of the forward primer may be sensitive to differences insequence between a target nucleic acid and a sibling nucleic acid.

The forward primer may also comprise a 5′ overhanging tail. The 5′ tailmay increase the melting temperature of the forward primer. The sequenceof the 5′ tail may comprise a sequence that is non-identical to thegenome of the animal from which the target nucleic acid is isolated. Thesequence of the 5′ tail may also be synthetic. The 5′ tail may comprise8, 9, 10, 11, 12, 13, 14, 15, or 16 nucleotides. The forward primer maycomprise SEQ ID NOS: 82-89; or variants thereof.

Reverse Primer

The PCR reaction may comprise a reverse primer. The reverse primer maybe complementary to a target nucleic acid. The reverse primer may alsocomprise a sequence complementary to an adaptor sequence. The sequencecomplementary to an adaptor sequence may comprise SEQ ID NO: 90 orvariants thereof.

Biochip

A biochip is also provided. The biochip may comprise a solid substratecomprising an attached probe or plurality of probes described herein.The probes may be capable of hybridizing to a target sequence understringent hybridization conditions. The probes may be attached atspatially defined addresses on the substrate. More than one probe pertarget sequence may be used, with either overlapping probes or probes todifferent sections of a particular target sequence. The probes may becapable of hybridizing to target sequences associated with a singledisorder appreciated by those in the art. The probes may either besynthesized first, with subsequent attachment to the biochip, or may bedirectly synthesized on the biochip.

The solid substrate may be a material that may be modified to containdiscrete individual sites appropriate for the attachment or associationof the probes and is amenable to at least one detection method.Representative examples of substrates include glass and modified orfunctionalized glass, plastics (including acrylics, polystyrene andcopolymers of styrene and other materials, polypropylene, polyethylene,polybutylene, polyurethanes, TeflonJ, etc.), polysaccharides, nylon ornitrocellulose, resins, silica or silica-based materials includingsilicon and modified silicon, carbon, metals, inorganic glasses andplastics. The substrates may allow optical detection without appreciablyfluorescing.

The substrate may be planar, although other configurations of substratesmay be used as well. For example, probes may be placed on the insidesurface of a tube, for flow-through sample analysis to minimize samplevolume. Similarly, the substrate may be flexible, such as flexible foam,including closed cell foams made of particular plastics.

The biochip and the probe may be derivatized with chemical functionalgroups for subsequent attachment of the two. For example, the biochipmay be derivatized with a chemical functional group including, but notlimited to, amino groups, carboxyl groups, oxo groups or thiol groups.Using these functional groups, the probes may be attached usingfunctional groups on the probes either directly or indirectly using alinker. The probes may be attached to the solid support by either the 5′terminus, 3′ terminus, or via an internal nucleotide.

The probe may also be attached to the solid support non-covalently. Forexample, biotinylated oligonucleotides can be made, which may bind tosurfaces covalently coated with streptavidin, resulting in attachment.Alternatively, probes may be synthesized on the surface using techniquessuch as photopolymerization and photolithography.

Diagnostics

As used herein the term “diagnosing” refers to classifying pathology, ora symptom, determining a severity of the pathology (grade or stage),monitoring pathology progression, forecasting an outcome of pathologyand/or prospects of recovery.

As used herein the phrase “subject in need thereof” refers to an animalor human subject who is known to have cancer, at risk of having cancer[e.g., a genetically predisposed subject, a subject with medical and/orfamily history of cancer, a subject who has been exposed to carcinogens,occupational hazard, environmental hazard] and/or a subject who exhibitssuspicious clinical signs of cancer [e.g., blood in the stool or melena,unexplained pain, sweating, unexplained fever, unexplained loss ofweight up to anorexia, changes in bowel habits (constipation and/ordiarrhea), tenesmus (sense of incomplete defecation, for rectal cancerspecifically), anemia and/or general weakness]. Additionally oralternatively, the subject in need thereof can be a healthy humansubject undergoing a routine well-being check up.

Analyzing presence of malignant or pre-malignant cells can be effectedin-vivo or ex-vivo, whereby a biological sample (e.g., biopsy) isretrieved. Such biopsy samples comprise cells and may be an incisionalor excisional biopsy. Alternatively the cells may be retrieved from acomplete resection.

While employing the present teachings, additional information may begleaned pertaining to the determination of treatment regimen, treatmentcourse and/or to the measurement of the severity of the disease.

As used herein the phrase “treatment regimen” refers to a treatment planthat specifies the type of treatment, dosage, schedule and/or durationof a treatment provided to a subject in need thereof (e.g., a subjectdiagnosed with a pathology). The selected treatment regimen can be anaggressive one which is expected to result in the best clinical outcome(e.g., complete cure of the pathology) or a more moderate one which mayrelieve symptoms of the pathology yet results in incomplete cure of thepathology. It will be appreciated that in certain cases the treatmentregimen may be associated with some discomfort to the subject or adverseside effects (e.g., damage to healthy cells or tissue). The type oftreatment can include a surgical intervention (e.g., removal of lesion,diseased cells, tissue, or organ), a cell replacement therapy, anadministration of a therapeutic drug (e.g., receptor agonists,antagonists, hormones, chemotherapy agents) in a local or a systemicmode, an exposure to radiation therapy using an external source (e.g.,external beam) and/or an internal source (e.g., brachytherapy) and/orany combination thereof. The dosage, schedule and duration of treatmentcan vary, depending on the severity of pathology and the selected typeof treatment, and those of skills in the art are capable of adjustingthe type of treatment with the dosage, schedule and duration oftreatment.

A method of diagnosis is also provided. The method comprises detectingan expression level of a specific cancer-associated nucleic acid in abiological sample. The sample may be derived from a patient. Diagnosisof a specific cancer state in a patient may allow for prognosis andselection of therapeutic strategy. Further, the developmental stage ofcells may be classified by determining temporarily expressed specificcancer-associated nucleic acids.

In situ hybridization of labeled probes to tissue sections may beperformed. When comparing the fingerprints between individual samplesthe skilled artisan can make a diagnosis, a prognosis, or a predictionbased on the findings. It is further understood that the nucleic acidsequence which indicate the diagnosis may differ from those whichindicate the prognosis and molecular profiling of the condition of thecells may lead to distinctions between responsive or refractoryconditions or may be predictive of outcomes.

Kits

A kit is also provided and may comprise a nucleic acid described hereintogether with any or all of the following: assay reagents, buffers,probes and/or primers, and sterile saline or another pharmaceuticallyacceptable emulsion and suspension base. In addition, the kits mayinclude instructional materials containing directions (e.g., protocols)for the practice of the methods described herein. The kit may furthercomprise a software package for data analysis of expression profiles.

For example, the kit may be a kit for the amplification, detection,identification or quantification of a target nucleic acid sequence. Thekit may comprise a poly (T) primer, a forward primer, a reverse primer,and a probe.

Any of the compositions described herein may be comprised in a kit. In anon-limiting example, reagents for isolating miRNA, labeling miRNA,and/or evaluating a miRNA population using an array are included in akit. The kit may further include reagents for creating or synthesizingmiRNA probes. The kits will thus comprise, in suitable container means,an enzyme for labeling the miRNA by incorporating labeled nucleotide orunlabeled nucleotides that are subsequently labeled. It may also includeone or more buffers, such as reaction buffer, labeling buffer, washingbuffer, or a hybridization buffer, compounds for preparing the miRNAprobes, components for in situ hybridization and components forisolating miRNA. Other kits of the invention may include components formaking a nucleic acid array comprising miRNA, and thus, may include, forexample, a solid support.

The following examples are presented in order to more fully illustratesome embodiments of the invention. They should, in no way be construed,however, as limiting the broad scope of the invention.

EXAMPLES Materials and Methods 1. Tumor Samples

127 renal tumor FFPE samples were obtained from the pathology archivesof Sheba Medical Center (Tel-Hashomer, Israel) and commercial sources(ABS Inc., Wilmington, Del., and BioServe™, Beltsville, Md.). The studyprotocol was approved by the Research Ethics Board of each of thecontributing institutes. FFPE samples were reviewed by a pathologistwith experience in urological pathology for histological type based onhematoxilin-eosin (H&E) stained slides, performed on the first and/orlast sections of the sample. Tumor classification was based on the WorldHealth Organization (WHO) guidelines (12). Tumor content was higher than60% for all the samples and higher than 80% for >80% of the samples.

The differential microRNAs were identified and the classificationalgorithm was trained using 71 samples including 21 oncocytoma samples,13 chromophobe samples, 17 conventional cell samples, and 20 papillarysamples. The classification algorithm was tested on an independent setof 56 samples including 19 oncocytoma samples, 14 chromophobe samples,17 conventional cell samples and 6 papillary samples

2. RNA Extraction

Total RNA was isolated as previously described (Rosenfeld et al., 2008).Briefly, seven to ten 10 μm-thick tissue sections were incubated a fewtimes in xylene at 57° C. to remove excess paraffin, then washed severaltimes with ethanol. Proteins were degraded by incubating the sample in aproteinase K solution at 45° C. for a few hours. RNA was extracted usingacid phenol/chloroform and then precipitated using ethanol; DNAses wereintroduced to digest DNA. Total RNA quantity and quality was measured byNanodrop ND-1000 (NanoDrop Technologies, Wilmington, Del.).

3. MicroRNA Profiling

Custom microarrays were produced by printing DNA oligonucleotide probesto more than 600 human microRNAs. Each probe, printed in triplicate,carries up to 22-nucleotide (nt) linker at the 3′ end of the microRNA'scomplement sequence in addition to an amine group used to couple theprobes to coated glass slides. 20 μM of each probe were dissolved in2×SSC+0.0035% SDS and spotted in triplicate on Schott Nexterion® Slide Ecoated microarray slides using a Genomic Solutions® BioRoboticsMicroGrid II according the MicroGrid manufacturer's directions. 54negative control probes were designed using the sense sequences ofdifferent microRNAs. Two groups of positive control probes were designedto hybridize to microarray (i) synthetic small RNA were spiked to theRNA before labeling to verify the labeling efficiency and (ii) probesfor abundant small RNA (e.g. small nuclear RNAs (U43, U49, U24, Z30, U6,U48, U44), 5.8 s and 5 s ribosomal RNA) were spotted on the array toverify RNA quality. The slides were blocked in a solution containing 50mM ethanolamine, 1M Tris (pH9.0) and 0.1% SDS for 20 min at 50° C., thenthoroughly rinsed with water and spun dry.

4. Cy-Dye Labeling of miRNA for Microarray

Five μg of total RNA were labeled by ligation (Thomson et al., NatureMethods 2004, 1:47-53) of an RNA-linker, p-rCrU-Cy/dye (Dharmacon), tothe 3′-end with Cy3 or Cy5. The labeling reaction contained total RNA,spikes (0.1-20 fmoles), 300 ng RNA-linker-dye, 15% DMSO, lx ligasebuffer and 20 units of T4 RNA ligase (NEB) and proceeded at 4° C. for 1hr followed by 1 hr at 37° C. The labeled RNA was mixed with 3×hybridization buffer (Ambion), heated to 95° C. for 3 min and than addedon top of the miRdicator™ array. Slides were hybridized 12-16 hr in 42°C., followed by two washes in room temperature with 1×SSC and 0.2% SDSand a final wash with 0.1×SSC.

Arrays were scanned using an Agilent Microarray Scanner Bundle G2565BA(resolution of 10 μm at 100% power). Array images were analyzed usingSpotReader software (Niles Scientific).

5. Array Data Normalization

The initial data set consisted of signals measured for multiple probesfor every sample. For the analysis, signals were used only for probesthat were designed to measure the expression levels of known orvalidated human microRNAs.

Triplicate spots were combined into one signal by taking the logarithmicmean of the reliable spots. All data was log-transformed and theanalysis was performed in log-space. A reference data vector fornormalization, R, was calculated by taking the mean expression level foreach probe in two representative samples, one from each tumor type.

For each sample k with data vector S^(k), a 2nd degree polynomial F^(k)was found so as to provide the best fit between the sample data and thereference data, such that R≈F^(k)(S^(k)). Remote data points(“outliers”) were not used for fitting the polynomials F. For each probein the sample (element S_(i) ^(k) in the vector S^(k)), the normalizedvalue (in log-space) Mk is calculated from the initial value S_(i) ^(k)by transforming it with the polynomial function F^(k), so that M_(i)^(k)=F^(k)(S_(i) ^(k)). Statistical analysis is performed in log-space.For presentation and calculation of fold-change, data is translated backto linear-space by taking the exponent.

6. Statistical Analysis

For every pair of groups (e.g., oncocytoma vs. chromophobe orconventional cell vs. papillary), microRNA expression was compared forall microRNAs that had expression level above background (mediannormalized fluorescence signal>700) in at least one of the two groups.P-values were calculated using a two-sided (unpaired) Student's t-teston the log-transformed normalized fluorescence signal. The threshold forsignificant differences was determined by setting a False Discovery Rate(FDR) of 0.1, to correct for effects of multiple hypothesis testing,resulting in p-value cutoffs in the range of 0.03-0.06. For eachdifferentially expressed microRNA we calculated the fold-difference(ratio of the median normalized fluorescence) and the area under curve(AUC) of the response operating characteristic (ROC) curve. Forclassification, two microRNAs with opposite specificity were chosen ateach decision point (FIG. 4), and their ratio of expression (ratio ofthe normalized fluorescence signal) was calculated for each sample. Athreshold level for the value of the ratios was determined using thetraining set of samples (indicated by the grey shaded regions in FIG.4BCD) by choosing the cutoff value with the smallest number ofclassification errors on the training set. These thresholds were used toclassify the test samples.

7. RT-PCR

RNA was incubated in the presence of poly (A) polymerase (Poly (A)Polymerase NEB-M0276L), MnCl₂, and ATP for 1 hour at 37° C. Then, usingan oligodT primer harboring a consensus sequence, reverse transcriptionwas performed on total RNA using SuperScript II RT (Invitrogen,Carlsbad, Calif.). Next, the cDNA was amplified by RT-PCR; this reactioncontained a microRNA-specific forward primer, a TaqMan (MGB) probecomplementary to the 3′ of the specific microRNA sequence as well as topart of the polyA adaptor sequence, and a universal reverse primercomplementary to the consensus 3′ sequence of the oligodT tail.

The cycle threshold (C_(T), the PCR cycle at which probe signal reachesthe threshold) was determined for each microRNA. To allow comparisonwith results from the microarray, each value received was subtractedfrom 50. This 50-C_(T) expression for each microRNA for each patient wascompared with the signal obtained by the microarray method.

Example 1 Specific microRNAs are Differentially Expressed BetweenDifferent Histological Subtypes of Kidney Tumors

127 formalin-fixed, paraffin-embedded (FFPE) samples of renal tumorswere collected, including 40 oncocytoma samples, 27 chromophobe samples,34 conventional (clear) cell samples, and 26 papillary tumor samples.The initial sample set used for biomarker identification and fortraining a classifier included 71 samples. Total RNA was extracted fromthese samples, and microRNA expression was profiled using microarrays.

We first looked for microRNAs that are differentially expressed betweendifferent histological subtypes of kidney tumors. We compared theexpression of microRNAs between oncocytoma samples (n=21), chromophobetumors (n=13), conventional cell tumors (n=17), and papillary tumors(n=20). More than 900 microRNAs were compared using statistical tests.MicroRNAs were considered differentially expressed between any twohistological types if their t-test significance (p-value) indicated aFalse Discovery Rate (FDR) below 0.1 and their median expression levelchanged at least 4-fold between the two groups (FIGS. 1-2). 33 microRNAswere identified as differentially expressed between different kidneytumors types (Table 2). To identify underlying similarities between thehistological types, the expression level of these 33 microRNAs was usedto cluster the 71 samples. This analysis identified four main clustersthat closely followed the predefined groups. Further, the expression ofmicroRNAs showed a high degree of similarity between conventional celland papillary tumors, and between chromophobe and oncocytoma, and alower degree of similarity between these pairs.

The clustering also identified groups of microRNAs with similarprofiles. Such co-regulated groups can hint to a possible effect ofupstream regulatory components. An analysis of predicted binding sitesof transcription factors near the start sites of co-regulated microRNAtranscripts generates a list of transcription factors that may beenriched for factors related to biological differences between thehistological types (Table 3).

Given the underlying biological similarities between the tumor types, wedecided to construct a classifier to identify kidney tumor subtype intwo steps, following the binary structure of the hierarchical clusteringtree: the first step identifies whether the sample belongs to one pairof types (chromophobe, oncocytoma) or to the other pair (conventionalcell, papillary); the second step decides between the two types in eachpair. The classifier therefore has three decision points, correspondingto the comparisons in Table 2. For each such decision point (or “node”),we chose two microRNAs: one that is highly expressed in one group, andanother that is more strongly expressed in the other group. MicroRNAswere selected based on their expression levels and distributions in thetraining set (Table 2), with the aim of selecting microRNAs that providea distinct difference in expression that can be used for accurateclassification. For identifying between the pair of types (chromophobe,oncocytoma) and the pair (conventional cell, papillary), we chosehsa-miR-221 (SEQ ID NO: 25) and hsa-miR-210 (SEQ ID NO: 20); foridentifying between chromophobe and oncocytoma, we chose hsa-miR-200c(SEQ ID NO: 34) and hsa-miR-139-5p (SEQ ID NO: 14); and for identifyingbetween papillary and conventional cell, we chose hsa-miR-31 (SEQ ID NO:4) and hsa-miR-126 (SEQ ID NO: 41) (FIG. 3). Using one microRNA fromeach set is sufficient to obtain a clear separation between the fourgroups (FIG. 3A), but to ensure better performance we used a combinationof two microRNAs at each point. Amongst this set of microRNAs, eachhistological type has high expression of at least two microRNAs (e.g.,hsa-miR-210 and hsa-miR-31 for papillary, or hsa-miR-221 andhsa-miR-200c for chromophobe), and low expression of at least two othermicroRNAs (FIG. 3).

We used the 71 samples of the training set to train a simple classifier,comprised of two steps and three pairs of microRNAs (FIG. 4). For eachpair of microRNAs, a threshold was determined on the ratio of theexpression levels of the two microRNAs (Methods)—this is equivalent to astraight line that separates two regions in log-space (see FIG. 4). Inthe first step (FIG. 4B), if the ratio of relative expression(normalized fluorescence) of hsa-miR-221 to relative expression(normalized fluorescence) of hsa-miR-210 is greater than the thresholdvalue of 9.86, the sample takes the left branch (FIG. 4A) and isidentified as either oncocytoma or chromophobe. If(hsa-miR-221/hsa-miR-210)<9.86, the samples takes the right branch (greyshaded region in FIG. 4B) and is identified as either conventional cellor papillary. In the second step, the same process is used—if(hsa-miR-200c/hsa-miR-139-5p)>33.1, the sample is classified aschromophobe (grey region in FIG. 4C), otherwise it is classified asoncocytoma. Alternatively, if (hsa-miR-126/hsa-miR-31)<2.32, the sampleis classified as papillary (grey region in FIG. 4D), otherwise it isclassified as conventional cell. In the training set, this classifiercorrectly identified 62 out of 71 samples, with an overall accuracy of87% (95% confidence interval: 77%-94%, assuming a binomialdistribution).

56 additional samples were collected as an independent test set (FIG.4). These samples were processed and their microRNA expression profileswere measured using the same protocols, more than two months after theinitial training set samples were profiled. The microRNA expressionprofiles of these samples were used to predict their histologicalsubtype according to the classification algorithm defined above (FIG.4). Of the 56 test samples, 52 samples were classified correctly andfour samples were classified incorrectly: two of the 14 chromophobesamples were classified as oncocytoma; one of the 19 oncocytoma sampleswas classified as chromophobe; one of the 17 conventional cell samplewas classified as oncocytoma; and all 6 papillary samples wereclassified correctly. Identification sensitivity was 95% for oncocytoma,86% for chromophobe, 94% for conventional cell, and 100% for papillary,with overall accuracy of 93% (95% confidence interval: 83%-98%, assuminga binomial distribution).

TABLE 2 Differentially expressed microRNAs Median (Conventional Cell +Micro values Papillary) vs. (Oncocytoma + Chromophobe vs. Papillary vs.RNA Oncotcy- Chromo- Conven- Chromophobe) Oncotcytoma Conventional Cellname toma phobe tional Papillary p-value fold AUC p-value fold AUCp-value fold AUC hsa- 50 5800 50 72 1.3E−03 14.38 0.67 8.0E−06 116.810.88 2.5E−01 1.45 0.64 miR- 141 hsa- 50 6500 50 130 2.7E−03 11.84 0.683.5E−05 129.68 0.88 1.3E−01 2.61 0.66 miR- 200c hsa- 72 730 84 792.7E−03 2.64 0.67 1.0E−06 10.23 0.92 5.6E−01 1.06 0.54 miR- 373* hsa-350 2700 200 350 1.0E−03 2.25 0.71 2.7E−06 7.56 0.92 1.7E−01 1.76 0.65miR- 637 hsa- 420 1600 220 300 5.2E−04 2.26 0.75 1.4E−05 3.73 0.911.4E−01 1.36 0.66 miR- 371-5p hsa- 390 1100 150 210 6.3E−06 3.66 0.793.2E−03 2.72 0.79 3.0E−01 1.39 0.61 miR- 557 hsa- 3900 1800 1200 6702.6E−05 3.2 0.8 6.8E−02 2.2 0.68 3.0E−01 1.74 0.6 miR- 193b hsa- 2700890 830 500 2.6E−04 2.49 0.78 9.2E−04 3.08 0.81 2.6E−02 1.67 0.7 miR-365 hsa- 21000 14000 29000 4200 4.9E−03 1.68 0.66 3.3E−03 1.57 0.815.0E−09 6.81 0.95 miR- 126 hsa- 790 100 120 50 2.7E−04 6.81 0.76 8.6E−057.75 0.87 1.6E−01 2.38 0.73 miR- 139-5p hsa- 52000 73000 5200 75004.1E−15 8.24 0.92 3.8E−03 1.42 0.74 6.3E−02 1.45 0.74 miR- 222 hsa-58000 81000 4000 8000 4.4E−17 10.95 0.93 1.9E−02 1.39 0.74 1.1E−02 20.81 miR- 221 hsa- 800 630 50 50 1.1E−17 14.39 0.94 1.3E−01 1.27 0.691.1E−01 1 0.6 miR- 221* hsa- 37000 24000 6100 14000 9.0E−07 4.16 0.869.2E−02 1.53 0.73 5.7E−03 2.24 0.84 miR- 10a hsa- 45000 59000 1300019000 5.1E−11 2.69 0.9 2.8E−01 1.32 0.63 5.2E−03 1.5 0.76 miR- 30b hsa-1400 1500 50 580 1.1E−06 5.8 0.81 8.1E−01 1.03 0.55 5.0E−04 11.66 0.81miR- 182 hsa- 400 720 50 50 4.3E−06 9.13 0.81 4.9E−02 1.82 0.68 9.5E−011 0.51 miR- 187 hsa- 1100 50 50 1300 2.8E−01 5.31 0.56 1.1E−02 21.1 0.752.1E−06 25.16 0.86 miR- 55 lb hsa- 50 50 70 790 3.9E−04 8.42 0.754.8E−01 1 0.6 1.7E−04 11.27 0.83 miR- 138 hsa- 50 330 250 14000 2.5E−0629.31 0.81 1.5E−02 6.63 0.73 2.5E−06 56.37 0.89 miR- 31 hsa- 430 210 3701100 9.1E−02 1.53 0.63 4.1E−01 2.05 0.58 7.0E−02 2.81 0.72 miR- 196bhsa- 1600 5500 3200 11000 3.5E−02 1.74 0.66 1.2E−02 3.38 0.82 8.0E−043.44 0.9 miR- 200a hsa- 2000 8200 2800 15000 9.5E−02 1.85 0.63 2.2E−034.13 0.81 3.3E−05 5.17 0.91 miR- 200b hsa- 50 220 5200 1600 4.6E−0821.24 0.84 5.4E−01 4.44 0.61 6.5E−02 3.28 0.74 miR- 192 hsa- 58 160 42002300 1.2E−07 20.63 0.83 4.2E−01 2.69 0.62 2.1E−01 1.87 0.69 miR- 194hsa- 120 98 1500 1200 1.8E−13 11.42 0.92 2.3E−01 1.27 0.58 2.0E−01 1.270.63 miR- 455-3p hsa- 350 310 1400 1900 3.4E−12 4.98 0.92 7.0E−01 1.130.55 2.8E−01 1.38 0.64 miR- 146a hsa- 50 50 1700 2700 5.1E−10 48.33 0.879.9E−01 1 0.53 4.9E−02 1.59 0.71 miR- 204 hsa- 280 370 11000 33001.2E−10 15.79 0.89 9.2E−01 1.32 0.52 8.4E−03 3.38 0.89 miR- 210 hsa-20000 21000 110000 180000 2.7E−10 7.79 0.88 4.4E−01 1.06 0.56 1.2E−011.56 0.68 miR- 21 hsa- 50 66 800 2000 1.4E−12 28.66 0.91 4.3E−01 1.320.63 6.9E−02 2.45 0.78 miR- 21* hsa- 77 160 1700 1200 6.5E−12 10.4 0.95.0E−01 2.01 0.55 3.7E−01 1.38 0.58 miR 146b- 5p hsa- 77 83 1300 6009.2E−09 9.65 0.85 9.5E−01 1.08 0.5 5.4E−02 2.23 0.72 miR- 155 Pair-wisecomparisons of each of the four histological types identified 33differentially expressed microRNAs. Here we show the p-value,fold-change of the median signal, and area under the ROC curve (AUC) foreach of these microRNAs in comparing papillary to conventional celltumors, oncocytomas to chromophobe tumors, and in comparing thecombination of conventional cell with papillary to the union ofchromophobe with oncocytoma.

TABLE 3 Association between co-expressed microRNAs and their predictedco-regulating transcription factors (TFs) Transcription Factor(s)MicroRNAs with predicted TF binding sites microRNAs upregulated inOncocytoma Ahr, Arnt, GR-alpha, GR-beta hsa-miR-139-5p, hsa-miR-365microRNAs upregulated in Oncocytoma and chromophobe tumors AR, Arnt,MEF-2A, NCX hsa-miR-10a and hsa-miR-221/222 Cdc5, POU3F2 (N-Oct-5a),POU3F2 hsa-miR-182, hsa-miR-221/222 (N-Oct-5b) c-Myc, Max1, SREBP-1a (b,c) hsa-miR-10a, hsa-miR-30b E4BP4, Hlf hsa-miR-221/222, hsa-miR-30bGATA-1, MZF-1 hsa-miR-10a, hsa-miR-182 LCR-F1 hsa-miR-182,hsa-miR-221/222, hsa-miR-30b POU3F2, TBP hsa-miR-10a, hsa-miR-182,hsa-miR-221/222 microRNAs upregulated in papillary tumors AREB6hsa-miR-196b, hsa-miR-200a/b, hsa-miR-31 C/EBPbeta hsa-miR-196b,hsa-miR-31 HNF-1A hsa-miR-200a/b, hsa-miR-31 POU2F1, Sp1, SRF, YY1hsa-miR-196b, hsa-miR-200a/b microRNAs upregulated in conventional celland papillary tumors AhR, AP-4, Arnt hsa-miR-192/4, hsa-miR-210,hsa-miR-455-3p AP-2alphaA, AP-2gamma hsa-miR-21, hsa-miR-210,hsa-miR-455-3p AR, AREB6, Nkx2-1 hsa-miR-192/4, hsa-miR-204 ATF6hsa-miR-192/4, hsa-miR-455-3p E47 hsa-miR-192/4, hsa-miR-204,hsa-miR-455-3p Elk-1 hsa-miR-204, hsa-miR-21 AP-2rep, FOXD1, MAZRhsa-miR-204, hsa-miR-210 GATA-1, NF-kappaB2, Sox9 hsa-miR-146a,hsa-miR-204 GR-alpha hsa-miR-210, hsa-miR-455-3p HSF1 (long), Meis-1hsa-miR-146a, hsa-miR-204, hsa-miR-210 HSF2, OCA-B, Octa-factor,octamer- hsa-miR-146a, hsa-miR-210 binding factor, Oct-B1(B2, B3),POU2F2 (2F2B, 2F2C, 3F1, 3F2, 4F1(1), 5F1A, 5F1B, 5F1C) ISGF-3, Pax-5,STAT1alpha, hsa-miR-204, hsa-miR-455-3p STAT1beta, STAT3 MEF-2Ahsa-miR-146a, hsa-miR-21, hsa-miR-455-3p NF-kappaB, NF-kappaB1hsa-miR-146a, hsa-miR-192/4, hsa-miR-204, hsa-miR-455-3p Pax-2hsa-miR-192/4, hsa-miR-204, hsa-miR-210, hsa-miR-455-3p POU2F1hsa-miR-146a, hsa-miR-192/4, hsa-miR-204, hsa-miR-210 PPAR-gamma1,PPAR-gamma2 hsa-miR-192/4, hsa-miR-204, hsa-miR-210 RelA hsa-miR-146a,hsa-miR-192/4, hsa-miR-455-3p

microRNAs were clustered according to a similar expression patternacross the 4 different renal tumor types as described above. TFs wereassociated to microRNAs following existence of predicted TF bindingsites in the microRNA promoter. The table lists only TFs which wereassociated to at least 2 co-expressed microRNAs. Several TFs in the samerow indicate that all TFs are associated to the same microRNAs in thatrow. microRNAs presented as hsa-miR-###/# (e.g hsa-miR-192/4), indicatethat the 2 microRNAs are located in the same genomic cluster andtherefore are predicted to be part of a shared pri-microRNA.

Example 2 Specific microRNAs are Able to Distinguish Between OncocytomaRenal Tumor Samples and Chromophobe RCC Samples

The analysis of the microarray results of oncocytoma renal tumor samplesversus chromophobe RCC samples are presented in Table 4. The resultsexhibited a significant difference in the expression pattern of severalmiRs. The normalized expression levels of hsa-miR-141 (SEQ ID NO: 32)and hsa-miR-200c (SEQ ID NO: 34) were found to be higher in chromophobeRCC samples in comparison to oncocytoma renal tumor samples. Thenormalized expression levels of hsa-miR-140-5p (SEQ ID NO: 28),hsa-miR-139-5p (SEQ ID NO: 14) and hsa-miR-551b (SEQ ID NO: 30) werefound to be higher in oncocytoma renal tumor samples in comparison tochromophobe RCC samples.

TABLE 4 miR SEQ Hairpin miR ID SEQ ID fold- median values name No NO.p-value change group 1 group 2 Up regulated in chromophobe RCC: hsa- 3233 6.60E−05 152.26 7.60E+03 5.00E+01 miR- 141 hsa- 34 35 2.10E−04 99.528.00E+03 8.00E+01 miR- 200c Down regulated in chromophobe RCC: hsa- 3031 2.10E−02 18.86 5.50E+01 1.00E+03 miR- 551b hsa- 14 15 1.60E−04 10.098.10E+01 8.10E+02 miR- 139-5p hsa- 28 29 8.20E−05 8.25 6.20E+01 5.10E+02miR- 140-5p miR name: is the miRBase registry name (release 10) p-value:is the result of unpaired two-sided t-test between the two groups ofsamples These miRs can be used to distinguish between oncocytoma renaltumor and chromophobe RCC. The classification could be conducted eitherwith a simple threshold (1 or 2 dimension threshold), a logisticregression model or any other classifier.

Example 3 Specific microRNAs are Able to Distinguish Between OncocytomaRenal Tumor Samples and Clear Cell RCC Samples

The analysis of the microarray results of oncocytoma renal tumor samplesversus clear cell RCC samples are presented in Table 5. The resultsexhibited a significant difference in the expression pattern of severalmiRs. The normalized expression levels of hsa-miR-551b (SEQ ID NO: 30),hsa-miR-182 (SEQ ID NO: 36), hsa-miR-221 (SEQ ID NO: 25), hsa-miR-222(SEQ ID NO: 26), hsa-miR-10a (SEQ ID NO: 48) and MID-00536 (SEQ ID NO:38) were found to be higher in oncocytoma renal tumor samples incomparison to clear cell RCC samples. The normalized expression levelsof hsa-miR-21 (SEQ ID NO: 47), hsa-miR-210 (SEQ ID NO: 20), hsa-miR-192(SEQ ID NO: 6), hsa-miR-194 (SEQ ID NO: 1), hsa-miR-146b-5p (SEQ ID NO:16), hsa-miR-155 (SEQ ID NO: 22) and hsa-miR-455-3p (SEQ ID NO: 24) werefound to be higher in clear cell RCC samples in comparison to oncocytomarenal tumor samples.

TABLE 5 miR SEQ Hairpin ID SEQ ID fold- median values miR name No NO.p-value change group 1 group 2 Up regulated in clear cell RCC:hsa-miR-192 6 7 1.30E−05 109.55 6.30E+03 5.70E+01 hsa-miR-194 1 2, 32.70E−05 78.06 5.10E+03 6.60E+01 hsa-miR-210 20 21 2.30E−07 37.81.20E+04 3.30E+02 hsa-miR- 16 17 2.60E−09 19.83 1.90E+03 9.70E+01146b-5p hsa-miR-155 22 23 5.10E−07 18.06 1.40E+03 8.00E+01 hsa-miR-455-24 49 1.90E−05 8.43 1.30E+03 1.50E+02 3p hsa-miR-21 47 11 1.70E−06 6.051.40E+05 2.30E+04 Down regulated in clear cell RCC: hsa-miR-182 36 372.20E−07 33.65 5.00E+01 1.70E+03 hsa-miR- 30 31 1.90E−05 23.48 5.00E+011.20E+03 551b hsa-miR-221 25 13 4.00E−10 11.81 6.50E+03 7.70E+04hsa-miR-222 26 27 3.20E−09 8.01 7.70E+03 6.20E+04 hsa-miR-10a 48 193.00E−07 5.79 7.50E+03 4.30E+04 MID-00536 38 39, 40 1.20E−13 5.495.20E+03 2.80E+04

Example 4 Specific microRNAs are Able to Distinguish Between BenignRenal Tumor Samples and Malignant RCC Samples

The analysis of the microarray results of benign renal tumor samplesversus malignant RCC samples are presented in Table 6. The resultsexhibited a significant difference in the expression pattern of severalmiRs. The normalized expression levels of hsa-miR-221 (SEQ ID NO: 25),hsa-miR-222 (SEQ ID NO: 26), hsa-miR-10a* (SEQ ID NO: 18),hsa-miR-139-5p (SEQ ID NO: 14) and hsa-miR-221* (SEQ ID NO: 12) werefound to be higher in benign renal tumor samples in comparison tomalignant RCC samples. The normalized expression levels of hsa-miR-21(SEQ ID NO: 47), hsa-miR-21* (SEQ ID NO: 10), hsa-miR-210 (SEQ ID NO:20), hsa-miR-192 (SEQ ID NO: 6), hsa-miR-194 (SEQ ID NO: 1),hsa-miR-146b-5p (SEQ ID NO: 16), hsa-miR-204 (SEQ ID NO: 8), hsa-miR-31(SEQ ID NO: 4), hsa-miR-155 (SEQ ID NO: 22) and hsa-miR-455-3p (SEQ IDNO: 24) were found to be higher in malignant RCC samples in comparisonto benign RCC samples.

TABLE 6 miR Hairpin SEQ ID SEQ ID median values miR name No NO. p-valuefold-change group 1 group 2 Up regulated in malignant RCC: hsa-miR-194 12, 3 1.10E−04 35.54 2.20E+03 6.30E+01 hsa-miR-31 4 5 2.80E−05 30.951.50E+03 5.00E+01 hsa-miR-192 6 7 1.20E−04 30.17 1.70E+03 5.50E+01hsa-miR-204 8 9 5.00E−04 27.22 1.40E+03 5.00E+01 hsa-miR-21* 10 112.20E−07 18.62 9.30E+02 5.00E+01 hsa-miR-146b-5p 16 17 8.10E−07 14.171.20E+03 8.50E+01 hsa-miR-210 20 21 1.70E−05 11.86 3.80E+03 3.20E+02hsa-miR-155 22 23 8.70E−05 8.84 7.00E+02 7.90E+01 hsa-miR-455-3p 24 491.40E−04 6.58 9.80E+02 1.50E+02 hsa-miR-21 47 11 2.30E−06 6.55 1.40E+052.10E+04 Down regulated in malignant RCC: hsa-miR-221* 12 13 8.90E−11 185.00E+01 9.00E+02 hsa-miR-139-5p 14 15 2.20E−09 15.9 5.30E+01 8.40E+02hsa-miR-10a* 18 19 2.00E−10 12.89 5.00E+01 6.40E+02 hsa-miR-221 25 136.70E−07 7.82 8.90E+03 7.00E+04 hsa-miR-222 26 27 1.00E−05 5.87 9.20E+035.40E+04

Example 5 Specific microRNAs are Able to Distinguish Between Clear CellRCC Samples and Chromophobe RCC Samples

The analysis of the microarray results of clear cell RCC samples versuschromophobe RCC samples exhibited a significant difference in theexpression pattern of several miRs, as indicated in Table 7. Thenormalized expression levels of hsa-miR-192 (SEQ ID NO: 6), hsa-miR-194(SEQ ID NO: 1) and hsa-miR-155 (SEQ ID NO: 22) were found to be higherin clear cell RCC samples in comparison to chromophobe RCC samples. Thenormalized expression levels of hsa-miR-141 (SEQ ID NO: 32) andhsa-miR-200c (SEQ ID NO: 34) hsa-miR-182 (SEQ ID NO: 36) and hsa-miR-187(SEQ ID NO: 43) were found to be higher in chromophobe RCC samples incomparison to clear cell RCC samples.

TABLE 7 miR Hairpin SEQ SEQ ID ID fold- median values miR name No NO.p-value change group 1 group 2 Up regulated in clear cell RCC:hsa-miR-192 6 7 6.30E−04 29.54 5.80E+03 2.00E+02 hsa-miR-194 1 2, 31.60E−03 29.24 4.90E+03 1.70E+02 hsa-miR-155 22 23 2.30E−05 22.841.50E+03 6.60E+01 Down regulatedin clear cell RCC: hsa-miR- 34 352.00E−06 181.94 5.00E+01 9.10E+03 200c hsa-miR-141 32 33 1.20E−06 175.155.00E+01 8.80E+03 hsa-miR-182 36 37 3.00E−05 36.53 5.00E+01 1.80E+03hsa-miR-187 43 44 3.20E−05 30.91 5.00E+01 1.50E+03

Example 6 Specific microRNAs are Able to Distinguish Between Clear CellRCC Samples and Papillary RCC Samples

The analysis of the microarray results of clear cell RCC samples versuspapillary RCC samples exhibited a significant difference in theexpression pattern of several miRs, as indicated in Table 8. Thenormalized expression levels of hsa-miR-126 (SEQ ID NO: 41) were foundto be higher in clear cell RCC samples in comparison to papillary RCCsamples. The normalized expression levels of hsa-miR-31 (SEQ ID NO: 4),hsa-miR-551b (SEQ ID NO: 30), hsa-miR-138 (SEQ ID NO: 50) andhsa-miR-204 (SEQ ID NO: 8) were found to be higher in papillary RCCsamples in comparison to clear cell RCC samples.

TABLE 8 miR SEQ Hairpin ID SEQ ID fold- median values miR name No NOp-value change group 1 group 2 Up regulated in clear cell RCC samples:hsa-miR-126 41 42 3.50E−11 5.35 2.60E+04 4.90E+03 Down regulated inclear cell RCC samples: hsa-miR-31 4 5 2.90E−06 38.42 4.50E+02 1.70E+04hsa-miR- 30 31 6.60E−08 32.03 5.00E+01 1.60E+03 551b hsa-miR-138 50 513.10E−06 17.04 5.70E+01 9.80E+02 hsa-miR-204 8 9 8.20E−04 5.77 6.20E+023.60E+03

Example 7 Specific microRNAs are Able to Distinguish Between ChromophobeRCC Samples and Papillary RCC Samples

The analysis of the microarray results of chromophobe RCC samples andpapillary RCC samples exhibited a significant difference in theexpression pattern of several miRs, as indicated in Table 9. Thenormalized expression levels of hsa-miR-141 (SEQ ID NO: 32) andhsa-miR-200c (SEQ ID NO: 34), hsa-miR-187 (SEQ ID NO: 43), hsa-miR-150*(SEQ ID NO: 52), hsa-miR-221 (SEQ ID NO: 25) and hsa-miR-222 (SEQ ID NO:26) were found to be higher in chromophobe RCC samples in comparison topapillary RCC samples. The normalized expression levels of hsa-miR-204(SEQ ID NO: 8), hsa-miR-31 (SEQ ID NO: 4), hsa-miR-21* (SEQ ID NO: 10),hsa-miR-551b (SEQ ID NO: 30), hsa-miR-21 (SEQ ID NO: 47), hsa-miR-194(SEQ ID NO: 1) and hsa-miR-455-3p (SEQ ID NO: 24) were found to behigher in papillary RCC samples in comparison to chromophobe RCCsamples.

TABLE 9 median miR Hairpin values miR name SEQ ID NO. SEQ ID NO. p-valuefold-change group 1 group 2 Up regulated in chromophobe RCC: hsa-miR-14132 33 1.40E−07 132.72 7.50E+03 5.70E+01 hsa-miR-200c 34 35 3.80E−06 70.37.80E+03 1.10E+02 hsa-miR-187 43 44 2.90E−07 24.59 1.20E+03 5.00E+01hsa-miR-150* 52 53 1.00E−07 16.87 2.00E+03 1.20E+02 hsa-miR-221 25 136.00E−10 11.13 8.60E+04 7.70E+03 hsa-miR-222 26 27 3.20E−09 9.9 6.90E+046.90E+03 Down regulated in chromophobe RCC: hsa-miR-204 8 9 1.30E−0749.96 5.40E+01 2.70E+03 hsa-miR-31 4 5 2.40E−04 42.6 3.50E+02 1.50E+04hsa-miR-21* 10 11 3.60E−08 38.3 6.10E+01 2.30E+03 hsa-miR-551b 3 0 312.10E−03 22.55 5.50E+01 1.30E+03 hsa-miR-21 47 11 4.90E−09 16.3 1.80E+042.90E+05 hsa-miR-194 1 2, 3 3.30E−03 14.36 1.50E+02 2.20E+03hsa-miR-455-3p 24 49 2.90E−10 11.73 9.30E+01 1.10E+03

Example 8 Specific microRNAs are Able to Distinguish Between OncocytomaRenal Tumor Samples and Papillary RCC Samples

The analysis of the microarray results of oncocytoma renal tumor samplesand papillary RCC samples exhibited a significant difference in theexpression pattern of several miRs, as indicated in Table 10. Thenormalized expression levels of hsa-miR-221* (SEQ ID NO: 12),hsa-miR-221 (SEQ ID NO: 25), hsa-miR-222 (SEQ ID NO: 26) and hsa-miR-126(SEQ ID NO: 41) were found to be higher in oncocytoma renal tumorsamples in comparison to papillary RCC samples. The normalizedexpression levels of hsa-miR-31 (SEQ ID NO: 4), hsa-miR-204 (SEQ ID NO:8), hsa-miR-21* (SEQ ID NO: 10), hsa-miR-21 (SEQ ID NO: 47) andhsa-miR-146a (SEQ ID NO: 45) were found to be higher in papillary RCCsamples in comparison to oncocytoma renal tumor samples.

TABLE 10 miR Hairpin SEQ SEQ median ID ID fold- values miR name NO: NO:p-value change group 1 group 2 Up regulated in oncocytoma: hsa-miR- 1213 7.50E−14 16.52 8.30E+02 5.00E+01 221* hsa-miR-221 25 13 1.20E−14 9.217.70E+04 8.30E+03 hsa-miR-222 26 27 2.70E−13 7.46 5.70E+04 7.60E+03hsa-miR-126 41 42 1.80E−12 5.47 2.40E+04 4.30E+03 Down regulated inoncocytoma: hsa-miR-31 4 5 1.10E−09 322.27 5.00E+01 1.60E+04 hsa-miR-2048 9 5.80E−10 61.41 5.00E+01 3.10E+03 hsa-miR-21* 10 11 1.30E−11 49.275.00E+01 2.50E+03 hsa-miR-21 47 11 4.10E−09 9.84 2.10E+04 2.10E+05hsa-miR- 45 46 2.00E−10 5.66 3.70E+02 2.10E+03 146a

Example 9 PCR Validation of Differentially Expressed microRNAs BetweenDifferent Histological Subtypes of Kidney Tumors

Of the 127 samples tested on the microarray, 32 formalin-fixed,paraffin-embedded (FFPE) samples of renal tumors were tested usingqRT-PCR, including 8 oncocytoma samples, 8 chromophobe samples, 8conventional (clear) cell samples, and 8 papillary tumor samples. Thesamples for qRT-PCR where randomly chosen within each group blinded totheir microarray signals.

Correlation between microarray and qRT-PCR results was assessed usingthe tree classifier that was devised based on microarray data (FIGS.5A-5C). Two microRNAs were used in each node. The correlation betweenthe log 2(ratio) of each such pair was checked in the microarray resultsand the inverted C_(t) difference between the two microRNAs in the PCRresults. The correlation between the ratio of hsa-miR-221 (SEQ ID NO:25) and hsa-miR-210 (SEQ ID NO: 20) in the two platforms was 0.92; thecorrelation between the ratio of hsa-miR-126 (SEQ ID NO: 41) andhsa-miR-31 (SEQ ID NO: 4) in the two platforms was 0.9 and thecorrelation between the ratio of hsa-miR-139-5p (SEQ ID NO: 14) andhsa-miR-200c (SEQ ID NO: 34) in the two platforms was 0.81.

The performance of the qRT-PCR classifier was checked on the 32 samplesusing Leave One Out Cross Validation (LOOCV). In each node, a logisticregression classifier was used. The accuracy of the qRT-PCR classifierwas 90.6%, and the sensitivity per histological type was:chromophobe—100%; Oncocytoma—100%; Clear cell carcinoma—88% andpapillary (chromaphil) tumors—75%.

TABLE 11 Sequences used in RT-PCR validation miR hairpin MGB FWD SEQ SEQSEQ SEQ miR- ID ID ID ID name NO: NO: MGB sequence NO: FWD sequence NO:hsa-  4  5 CCGTTTTTTTTTTTTCAGCTATG 76 CAGTCATTTGGGGGCAAGATGCTGGCAT 82miR-31 hsa- 41 42 CCGTTTTTTTTTTTTCGCATTAT 77CAGTCATTTGGGTCGTACCGTGAGTAAT 83 miR- 126 hsa- 14 15CCGTTTTTTTTTTTTCTGGAGAC 78 CAGTCATTTGGCTCTACAGTGCACGTGT 84 miR- 139-5phsa- 20 21 CGTTTTTTTTTTTTCAGCCGCT 79 CAGTCATTTGGGCTGTGCGTGTGACAGC 85miR- 210 hsa- 34 35 CGTTTTTTTTTTTTCCATCATT 80CAGTCATTTGGGTAATACTGCCGGGTAA 86 miR- 200c hsa- 25 13CGTTTTTTTTTTTTGAAACCCA 81 CAGTCATTTGGGAGCTACATTGTCTGCT 87 miR- 221Reverse 90 GCGAGCACAGAATTAATACGAC primer U6 AATATGGAACGCTTCACG 88GCAAGGATGACACGCAAATTC 89

The foregoing description of the specific embodiments so fully revealsthe general nature of the invention that others can, by applying currentknowledge, readily modify and/or adapt for various applications suchspecific embodiments without undue experimentation and without departingfrom the generic concept, and, therefore, such adaptations andmodifications should and are intended to be comprehended within themeaning and range of equivalents of the disclosed embodiments. Althoughthe invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

It should be understood that the detailed description and specificexamples, while indicating preferred embodiments of the invention, aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the invention will becomeapparent to those skilled in the art from this detailed description.

1.-3. (canceled)
 4. A method for classifying a kidney tumor sampleobtained from a human subject as benign kidney tumor or malignant renalcell carcinoma (RCC) and administering a treatment to the human subjectbased on the classification of the kidney tumor sample, the methodcomprising: (a) obtaining a kidney tumor sample from a human subject;(b) determining the expression profile of SEQ ID NOS: 1, 4, 6, 8, 10,12, 14, 16, 18, 20, 22, 24-26, 28, 30, 32, 34, 36, 38, 41, 45, 47, 48,56, 58, 60, 62, 66 or any combination thereof in the sample; (c)comparing said expression profile to one or more reference values; (d)classifying the kidney tumor sample as benign kidney tumor or malignantRCC based on the expression of SEQ ID NOS: 1, 4, 6, 8, 10, 12, 14, 16,18, 20, 22, 24-26, 28, 30, 32, 34, 36, 38, 41, 45, 47, 48, 56, 58, 60,62, 66 or any combination thereof in the sample relative to thereference value; and (e) administering a treatment to the human subjectbased on the classification of the kidney tumor sample as benign kidneytumor or malignant RCC.
 5. The method of claim 4, wherein an increase inthe expression of SEQ ID NOS: 12, 14, 18, 25, 26, 28, 30, 36, 38, 41,48, 66 or any combination thereof in the sample relative to thereference value is indicative of the presence of benign kidney tumor. 6.The method of claim 4, wherein an increase in the expression of SEQ IDNOS: 1, 4, 6, 8, 10, 16, 20, 22, 24, 32, 34, 45, 47, 56, 58, 60, 62, orany combination thereof in the sample relative to the reference value isindicative of the presence of malignant RCC.
 7. The method of claim 4,wherein the malignant RCC is chromophobe RCC and the benign kidney tumoris oncocytoma, wherein the method comprises: determining the expressionprofile of SEQ ID NOS: 14, 28, 30, 32, 34, 41, 56, 58, 60, 62, 66 or anycombination thereof in the sample and; whereby an increase in theexpression of SEQ ID NOS: 14, 28, 30, 41, 66 or any combination thereofin the sample relative to the reference value or values is indicative ofthe presence of oncocytoma.
 8. (canceled)
 9. The method of claim 4,wherein the malignant RCC is chromophobe RCC and the benign tumor isoncocytoma, wherein the method comprises the step of determining theexpression profile of SEQ ID NOS: 14, 28, 30, 32, 34, 41, 56, 58, 60,62, 66 or any combination thereof, and wherein an increase in theexpression of SEQ ID NOS: 32, 34, 56, 58, 60 or any combination thereofin the sample relative to the reference values is indicative of thepresence of chromophobe RCC.
 10. The method of claim 4, wherein themalignant RCC is clear cell RCC and the benign tumor is oncocytoma,wherein the method comprises: determining the expression profile of SEQID NOS: 1, 6, 16, 20, 22, 24-26, 30, 36, 38, 47-48 or any combinationthereof in the sample; and whereby an increase in the expression of SEQID NOS: 25-26, 30, 36, 38, 48 or any combination thereof in the samplerelative to the reference values is indicative of the presence ofoncocytoma.
 11. (canceled)
 12. The method of claim 4, wherein themalignant RCC is clear cell RCC and the benign kidney tumor isoncocytoma, wherein the method comprises the step of determining theexpression profile of SEQ ID NOS: 1, 6, 16, 20, 22, 24-26, 30, 36, 38,47-48 or any combination thereof, and wherein an increase in theexpression of SEQ ID NOS: 1, 6, 16, 20, 22, 24, 47 or any combinationthereof in the sample relative to the reference values is indicative ofthe presence of clear cell RCC. 13-21. (canceled)
 22. A The method ofclaim 4 wherein the malignant RCC is papillary RCC and the benign kidneytumor is oncocytoma, wherein the method comprising comprises:determining the expression profile of SEQ ID NOS: 4, 8, 10, 12, 25-26,41, 45, 47 or any combination thereof in the sample; and whereby anincrease in the expression of SEQ ID NOS: 12, 25, 26, 41 or anycombination thereof in the sample relative to the reference values isindicative of the presence of oncocytoma.
 23. The method of claim 4,wherein the malignant RCC is papillary RCC and the benign kidney tumoris oncocytoma, wherein the method comprises the step of determining theexpression profile of SEQ ID NOS: 4, 8, 10, 12, 25-26, 41, 45, 47 or anycombination thereof, and wherein an increase in the expression of SEQ IDNOS: 4, 8, 10, 45, 47 or any combination thereof in the sample relativeto the reference values is indicative of the presence of papillary RCC.24-40. (canceled)
 41. A kit for renal tumor classification, said kitcomprises a probe comprising a nucleic acid sequence that iscomplementary to anyone of SEQ ID NOS: 1, 4, 6, 8, 10, 12, 14, 16, 18,20, 22, 24-26, 28, 30, 32, 34, 36, 38, 41, 45, 47, 48, 56, 58, 60, 62,66 or any combination thereof. 42-46. (canceled)
 47. The method of claim4, wherein the kidney tumor sample is a body fluid, a cell line or atissue sample.
 48. The method of claim 47, wherein the tissue sample isa fresh, frozen, fixed, wax-embedded or formalin fixed paraffin-embedded(FFPE) tissue.
 49. The method of claim 4, wherein the expression profileis determined by nucleic acid hybridization, nucleic acid amplification,or a combination thereof.
 50. The method of claim 49, wherein thenucleic acid hybridization is performed using a solid-phase nucleic acidbiochip array.
 51. The method of claim 49, wherein the nucleic acidhybridization is performed using in situ hybridization.
 52. The methodof claim 49, wherein the in situ hybridization method compriseshybridization with a probe.
 53. The method of claim 52, wherein theprobe comprises a nucleic acid sequence that is complementary to SEQ IDNOS: 1, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24-26, 28, 30, 32, 34, 36,38, 41, 45, 47, 48, 56, 58, 60, 62, 66 or any combination thereof. 54.The method of claim 49, wherein the nucleic acid amplification method isreal-time PCR (RT-PCR), and the RT-PCR method comprising forward andreverse primers.
 55. The method of claim 52, wherein the forward primercomprises any one of SEQ ID NOS: 82-87.
 56. The method of claim 52,wherein the reverse primer comprises SEQ ID NO:
 90. 57. The method ofclaim 52, wherein the RT-PCR method further comprises hybridization witha probe.
 58. The method of claim 57, wherein the probe comprises anucleic acid sequence that is complementary to any one of SEQ ID NOS: 1,4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24-26, 28, 30, 32, 34, 36, 38, 41,45, 47, 48, 56, 58, 60, 62, 66 or any combination thereof.
 59. Themethod of claim 57, wherein the probe comprises any one of SEQ ID NOS:76-81.